Wileyfox phones

Wileyfox UK launches new smart phone for the savvy open market



Wileyfox is a London-based company which deals on high-value, affordable and low-choice mobile phones, including phone-specific screen protectors, clip-on covers and spare batteries. It was established in 2015 (John., 2018).

Wileyfox operates with few workers and has no legacy devices to compete against market leaders such as Apple and Samsung. To effectively differentiate its products, the British phone maker recently announced hardware commoditization as a strategic roadmap to achieving competitive advantage against Android or iOS-reliant rivals (Woods., 2015).

Mike Coombes holds position as Wileyfox CEO and has experience as a former sales executive at Nokia and HTC (O’Reilly., 2013).

Figure 1: Wileyfox Phones

Source: Wileyfox (2019)


On 6 February, 2018, Wileyfox went declared bankruptcy and publicized on 19 March that Santok Group has acquired license to sell its phones in South Africa and Europe. However, as a struggling new entrant in the overcrowded and advanced global markets, Wileyfox aims at maximizing profit potentials by introducing unique multifunctional handsets that guarantee consumer satisfaction through durability, long battery life, user privacy and 3D viewing (Brown., 2018).

The new product will be available worldwide within 2 years after its launch in April 2019. This brief marketing proposal is a sample prepared ahead of the product launch.


Considering its efficient assembly procedures, design skills, cost control, and adaptability to changing market demands, Wileyfox can achieve competitiveness and sustainable growth if management adopts push and pull strategies in its value chain. But the undercapitalized phone maker is liquidating assets and are yet to pay of their debts—an indication of its inability to sustain long-term business. In addition, its Swift and Storm device owners are doubtful of the company’s existence and growth considering that their previous operating system Cyanogen (an Android variant) was problematic. According to Woods (2015), consumers are expecting huge investments in research and development (R&D).

Figure 1: The U.K. phone market

Source: Statistica (2019)

As shown in Figure 1, Wileyfox maintains an insignificant percentage of the U.K. phone market. Thus, its marketing priorities are:

  • To expand customer base through strategic advertising
  • To build solid business relationships with telecom operators
  • To generate sales and achieve incremental profit after 12 months
  • To invest in corporate social responsibility
  • To respect consumer protection laws and
  • To conduct business with an environmentally-friendly approach.

The fictional smartphone, Blaze 3D Plus, has an innovative, thin and user-friendly design, with a biometric security system, long-lasting solar battery, and an eco-friendly source of power that offers at least 12 hours of talk-time. It comes with a 2-year warranty, which highlights Wileyfox’s focus on quality, affordability and consumer satisfaction. Additionally, the new phone will be usable with any telecom carrier—on pre-paid plans. It will offer larger, durable touchscreen, drop-proof quality, free-calling online services, call recording capability, 15-megapixel camera, among other functions and benefits that outclass competitors.

Table 1: Features and Benefits of Blaze 3D Plus

Source: The Author (2019)


As shown in Table 1, the cost-efficient Blaze 3D Plus will provide convenience for travellers, with added entertainment programs for all customers. The primary target segment (Wedel., 2000) comprises of middle and upper-income customers(professionals) who don’t have charging time and therefore prefer long-lasting batteries for uninterrupted usage. This group also includes people whose annual income is above $80,000.

Secondary targets are young adults aged between 18 – 30 years old, who can afford new, appealing gadgets—especially phones with alternative energy sources and unique features that can be traded for newer models at little costs.


Market trends between 2014 – 2018 show significant increase in smartphone sales, and users are shifting from traditional handsets, which offer limited features, to iOS and Android-powered cell phones with virtual keyboards and touchscreen interface. Table 2 shows total shipment volume of 1,204.4 million and 1,738.5 million in 2014 and 2018 respectively. The 11.5% increase is a proof of the unlimited opportunities in global smartphone markets, which Wileyfox should maximize with its launch of Blaze 3D Plus.

Table 2: Global Smartphone Sales & Market Share (2014-18)

Source: IDC Worldwide Mobile Phone Tracker (2019)

*Units in Millions

Figure 2: Global Market Share of Smart-Connected Devices (2013-18)

Source: IDC Worldwide Mobile Phone Tracker (2019)


Wileyfox has to build on its strengths with the launch of Blaze 3D Plus. Product differentiation, as highlighted in Table 1, will certainly provide differential advantage and build positive brand image for the company.

However, major challenges include poor visibility in global markets, lack of investment capital, limited distribution networks etc. Until Blaze 3D Plus is thoroughly tested and found to be safe and compatible with telecom networks, Wileyfox cannot expect a favourable competition (Burrnett., 2007).

Table 3: SWOT Analysis of Wileyfox

Source: The Author (2019)


Samsung, Huawei and Apple present Wileyfox with its greatest challenges. Others in the highly competitive smartphone market are Chinese brands—Xiaomi and OPPO (Reith & Chau., 2018). Wileyfox should therefore adopt the “bring-your-own-gadgets” at work to prove their credibility, show compliance to corporate policies that protect consumer rights, and increase sales. However, most first-time smartphone buyers may not choose Blaze 3D Plus because of its sophistication.


Introducing new profitable products in the highly-competitive smartphone industry comes with opportunities and stress-prone planning activities for the launch team, especially in the pre-launch phase where cutting corners or ignoring something can jeopardize realization of business objectives. For example, there should be a comprehensive list of activities with names/groups attached to departments and responsibilities, including links for inter-departmental functioning and external support.

The product development and product launch phase are crucial for goals attainment and sustainability. Wileyfox Corporate Executive Board should therefore take cognizance of these 3 major challenges: product planning; communications (internal); and uniform implementation of product launch objectives by employees in marketing, sales, engineering, customer support and supply chain.

Product Launch Plan: This stage requires a strategic and comprehensive product development idea, which must be separate and segmented into:

  1. Initial planning, ii. Pre-launch activities, iii. Sales and channel launch preparedness, iv. Launch date, v. Post-launch supervision.

Initial planning should take between 3-6 months before the product launch date, and the key objectives should be to:

  • Increase sales volume from existing customers
  • Discover new markets
  • Maximize use of new technology

Pre-launch activities should take about 3 months before the product launch date, with key activities focused on: developing a timeline that indicates topics, themes, deliverables and timing, including appropriate marketing channels for promoting the launch (email, intranet, screensavers, newsletters, corporate blog, face-to-face meetings etc); creating reference terminology for the new product; formulating creative visual aspects of branding for the product; planning strategic marketing activities and key launch events; finalizing pricing and distribution channels; organizing sales training activities/materials and sales collateral; and using print/media advertising etc to create pre-launch awareness.

Sales and channel launch preparedness should take about 1 month ahead of launch date. It requires training/advising of the launch team on key activities/events, roles and responsibilities; availability of sales tools/collateral; explaining the process; and offering support.

On the launch date, the company’s planning and preparedness are tested from press releases to live media update on the public unveiling usually held at a trade centre. After this, the new products go on sale while business managers monitor market trends for performance and policy adjustments—where necessary. Two-way communication should be encouraged and, importantly, negative comments should be embraced.

Pre-launch marketing Objectives

  1. Increase brand awareness among targeted segments by 50%
  2. Build strong relationships with distributors, retailers and telecom carriers within 100 days
  3. Earn the British government’s recognition for power efficiency before launch
  4. Advertise the benefits of solar-powered battery
  5. Invest in relationship-building activities with host communities, NGOs and charity organizations.

Steps to launching a new product:


According to a 2012 Babson College survey, only about 65% firms attain profitability in their product launch, and nearly 15% of businesses fail due to non-use of quantifiable performance measures until post-launch. These results highlight the importance of planning, strategy, and continuous market assessment even when sales are increasing. In the first 12 months after launching Blaze 3D Plus, Wileyfox management should answer these questions:

  • Did the product launch survive development, manufacturing, administrative and financial setbacks?
  • Was the timing right?
  • How did the target market respond?
  • How did the sales team perform in terms of feedback, use of promotional literature, training materials, and marketing tools, including feedback from customers?
  • Were the customer support channels established before product launch?

Post-launch Marketing Objectives

  1. Achieve unit sales volume of 240,000 within first 12 months
  2. Increase brand awareness across business areas by 20% within 12 months
  3. Restrict losses to less than $1 million
  4. Surpass the break-even mark within 12 months
  5. Achieve at least 10% annual ROI within 12 months
  6. Recycle over 40% of phone parts after life span/warranty period.

Wileyfox can achieve its goals through effective marketing mix decisions such as:

Product: Differentiating Blaze 3D Plus with features that are inimitable.

Price: Offering low prices, discounts and flexible payment options to selected segments/markets. Average retail and wholesale prices should be $150 and $135 respectively.

Promotion: Using promotional activities (e.g. print/media advertising and sponsorship of sports, educational, entrepreneurship or social events.

Place: Discovering new markets and maximizing distribution channels to ensure a balance between demand and supply.

Table 4: Financials/Forecasts

Source: The Author (2019)

Table 5: Proposed Marketing Activities

Source: The Author (2019)

Depending on the impact of these marketing activities on financials, there may be need for revision and adjustment to protect market share and maintain sustainable growth.


Although business is about experimenting, learning and adjusting; to avoid risks from losing market share to contenders, Wileyfox should form mergers and use a strong PR while its affiliate, Santok Group, maximizes local distribution channels in Europe, Asia and other continents where brand presence is currently insignificant.


Brown S., 2018, “UK phone maker Wileyfox enters bankruptcy,” AndroidAuthority, accessed 26.01.2019, < https://www.androidauthority.com/wileyfox-enters-administration-bankruptcy-835948/>

Burrnett J., 2007, “Product Differentiation,” John Wiley & Sons Inc., New Jersey, accessed 28.01.2019

John M., 2018, “Wileyfox is back: New handsets arriving this year and more good news for owners,” TechRadar, viewed 26.01.2019, < https://www.techradar.com/news/wileyfox-is-back-new-handsets-arriving-this-year-and-more-good-news-for-owners>

O’Reilly L., 2013, HTC UK in turmoil following senior exits,” MarketingWeek, retrieved 27.01.2019, < https://www.marketingweek.com/2013/03/11/htc-uk-in-turmoil-following-senior-exits/>

Reith R. & Chau M., 2018, “Smartphone Market Share,” IDC, retrieved 28.01.2019

Wedel M., “Historical Development of the Market Segmentation Concept.” pp. 3-5, Norwell, USA: Kluwer Academic Publisher, retrieved 28.01.2019

Woods B., 2015, “Wileyfox launches its first Cyanogen OS smartphones, the 5’’ Swift and 5.5’’ Storm,” TheNextWeb, accessed 27.01.2019

Laycon has a heart of gold

Big Brother Naija, BBNaija housemate, Laycon, has reacted to Erica’s disqualification from the Lockdown house.

Erica was disqualified from the show after she got third strike from Biggie for violating the house rules.

Erica got disqualified after Biggie gathered the housemates at the lounge and played videos of when she threatened to kill Laycon outside the house and hurled insults at him.

Also, video of her pouring water on the Head of House (HoH) bed in order to stop Prince who is her deputy from sharing the lounge with her was highlighted.

Reacting to her disqualification, Laycon in a conversation with Kiddwaya and Vee said he feels sorry for her, saying her actions were because she was drunk.

When asked if he feels responsible for her eviction, Laycon said no, adding that she’s the architect of her predicament.

Laycon said: “She was talking , all the things she said and action caused her eviction.

“I felt she was drunk and said all those things.”

Erica’s disqualification from the reality show alongside Lucy’s eviction has left nine housemates in the house to battle for the grand prize of N85m.

BBN 2020: Why Erica was disqualified

Big Brother Naija, Erica, was not interviewed by host Ebuka Obi-Uchendu, following her disqualification on Sunday.

The organizers sent the ex-Miss Nigeria contestant packing, after she was issued a third strike.

Before this weekend, Erica had two strikes. Biggie issued her the first for whispering and speaking in codes while communicating.

She was given a second strike for sleeping in the HoH room until 3am with Kiddwaya.

Then, on Sunday morning, she poured water on the HoH bed, to prevent her Deputy, Prince, from sleeping in it.

This contravenes Biggie’s rules, which stipulates both Head of House and their deputies must sleep on the same bed for the week.

After she was eventually disqualified, Erica was not allowed to make any remarks in a live interview with Ebuka, as it is with the show’s tradition.

It is understood that the organizers will not allow her do official media rounds with sponsors.

She becomes the fifth Big Brother Naija housemate to be disqualified in the history of the show, after Tasha and Kemen.

10 things you should never do while hungry

Hello friends,

Below are 10 things you should never do on an empty stomach:

1. Do not take medicines (drugs): You should normally not have medicines on an empty stomach, especially when they are anti-inflammatory. Well, unless your prescription says you are to take in on an empty stomach. Therefore, consult your doctor to find out if there are any prior food recommendations.
2. Do not take alcohol: Drinking alcohol on an empty stomach will hit you harder because the rate of alcohol absorption in your body will be high. It will affect your heart, liver as well as kidney. So before you celebrate make sure your stomach is at least half filled.
3. Do not take a decision: Taking a decision on empty stomach may alter your performance and may leave you feeling regretful later on.
4. Do not drink coffee: Drinking coffee on an empty stomach is not advisable. It is better to make sure there’s something in your stomach before having your much needed first cup of coffee. Drinking coffee on an empty stomach can lead to acid reflux.
5. Do not chew gum: Chewing gum often over a long period of time on an empty stomach can lead to inflammation or gastritis in the stomach. Never chew gum for more than 10-15 minutes. Chewing gum on an empty stomach can have a negative effect on your digestion. Research has shown that people who chew gum will go for an unhealthy snack much quicker than people who don’t.
6. Do not sleep: This one doesn’t happen to us very often, but it turns out that sleeping on an empty stomach is not a good idea. You’ll have more trouble falling asleep, wake up earlier than usual and you also can’t get into a deep sleep, which means you’ll run a higher risk of sleep deprivation. Your body has to compensate for this lack of sleep with a higher energy intake, which means you’ll crave more food during the day.

7. Do not drink fresh juice: Your stomach has trouble processing the acids and fibers in a glass of fresh juice when there isn’t something lining its bottom already. If you drink juice on an empty stomach on a regular basis, this can even lead to gastritis. Really want a glass of fresh juice anyway? Then it’s best to dilute it with some water.
8. Avoid intense workout: We have this imaginary saying in our minds that it’s great to exercise on an empty stomach and that it might actually help you burn more fat which may be true on some level but I assure you that this is quite unhealthy. It’s better to at least fill your system with nuts or water before you hit the gym. And as for intense workouts, it is a must to fuel your body with sufficient food. Working out on an empty stomach can result in a loss of muscle. When your workout is intense, and your body has no enough glycogen to fuel, your muscles start burning instead of the fat. So start giving your body some fuel to burn.
9. Do not negotiate: If you are functioning low in fuel, you will not be able to do negotiations in the best way possible.
10. Avoid too much spicy food: Yes, spicy food is so good to taste but then it’s too much for your stomach to handle. Your stomach is a delicate part, and spicy food can hurt it. So you need to balance it by mixing it with less spicy or regular food, so it is well balanced for your stomach to digest. Tasty food on an empty stomach can cause heart burn and may even upset your system. So always remember to keep it well balanced.

Health is wealth. Therefore, it is very important to consult a healthcare professional for all your healthcare needs.

How to write a job-winning CV

Writing your CV has changed over the last ten years.

Gone are the days when you wrote your CV to impress the hiring manager. Today you’re writing your CV to pass an applicant tracking system (ATS), and then the hiring manager review.

I’ve discussed extensively across this website how to write your CV to pass an ATS by using job advert and job description keywords, but what do hiring managers really want to see on your CV? Below 15 hiring managers tell you what they want to see on a candidate’s CV and what tips you should be following when you’re writing your CV.

How To Write Your CV

Hiring Manager One – While there are many issues to consider when preparing a CV, my personal top three tips would be;

  • Focus on achievements; the recruiter is always more interested in the value-added in previous roles rather than a simple tick list of skills
  • Make the CV relevant to the position; one size does not fit all
  • Presentation and proofreading is critical; grammatical errors and poor layouts make it easy for a recruiter sift through multiple applications

Hiring Manager Two – Be specific about measuring any performance in your role. Too many times, CVs have a list of responsibilities without giving detail on how individually that person has contributed, influenced or performed within their remit. Examples include:

  • Whether it is bringing down debtors days by “X” thus improving cash flow by “Y” allowing the business to invest that into a new “Z”
  • Introduced a new sales and client management process which led to an increase revenue from “a” to “b” and an improvement in over all profitability to “C”
  • Reducing costs across the group from “X” to “Y” saving “Z” by implementing a new STP

Hiring Manager Three – Beyond the obvious requirements of correct, spelling, grammar and punctuation, a great resume tells a story. It should convey the individual’s career progression, increased responsibility along the continuum, and focus within the career path.

Where ever possible, accomplishments should be quantifiable. The best resumes whet the appetite of the reader and create enough interest to make he or she want to find out more.

Hiring Manager Four – I advise candidates to recognize that we live in a skills-based-hiring world. And while I’m no fan of this hiring approach, I suggest they edit CV’s to contain titles, duties, and keywords or phrases, and experiences listed in the job ad.

The harder part of CV writing involves sharing other facets of your personality without sounding too scattered. Recruiters want to “type” people into a job category, so fitting that category is important. At the same time, showing a little personality helps, but don’t overdo it. Striking a balance can be tricky.

Hiring Manager Five – The first half of the first page of your CV, is what will attract and hold interest. Check your grammar, spelling and presentation. Focus on the employers needs not your own, while selling yourself but be honest. The first part of your CV is about ability, quality and achievement, while the second part of your CV should evidence what you do or did.

Hiring Manager Six – Your CV should focus on your “quantifiable” achievements in your last three roles or previous 5-to-8 years. A good CV also explains to the future employer what it is you learnt in each position and how that’ll impact their business. Tailor all CV’s to the position you’re applying for.

Hiring Manager Seven – Most successful recruiters see hundreds if not thousands of resumes each week. Many do not spend more than a few seconds on each – searching for something that will shed light on the quality of the person in front of them, and their ability to do the job they are searching for.

The area I find to be extremely important on a resume is the the top 1/3 of the resume. This is the place that a candidate should draw me in, and get me excited to read more. No matter if its a “career highlights” section, “relevant projects” or even “education” – whatever you feel will help me get to know you and why you are the best fit for the role I am hiring for – this is where it should be listed.

Hiring Manager Eight – My thoughts – Your CV should try to give the impression to the reader of what kind of person you are. Yes your career is important, but what about your hobbies, what books he/she has read recently, opinion about the industry the company operates in.

Hiring Manager Nine – Maximum of three pages if you have a decent amount of experience;

  • The first page is really important so make sure it’s really sharp with first third of the page a summary and profile about you and what makes you tick, covering your values, character, and what you like doing the most, and any relevant ambitions you have. Then a short section on key skills bullet points relevant to the job you you would like to be considered for.
  • Then the final third of first page, be really clear about dates, job title talk about the last two roles or positions held and clearly explain the challenge you undertook, the results you achieved and what you had to do to achieve those results, i.e. what added value did you bring.
  • Page two and three if necessary, the same, clear on dates and job title. Then for each role be clear about the challenge undertaken and the results achieved in the role. Do mention relevant targets met or exceeded. Do mention individual responsibility AND team work where appropriate.
  • Finally name and contact number is usually enough, leave out DOB, hobbies, esp property development! How many children you have is not relevant!

Hiring Manager Ten – My view would be that candidates have to get into the minds of the people making decisions, these people not having personal knowledge of the individual applying, or potentially the deep technical knowledge of the area within which the applicant specializes.

Too many candidates are too thin on the information provided or assume people reading their profiles will really look into who they are and read between the lines on their CV. Your CV is your chance to make yourself a must interview candidate and your profile must leave nothing to chance or assumption from the client side.

You must tick all the boxes that a decision maker will look for when looking over multiple profiles without the time to really look into who the candidate is, and sell yourself over the competition.

Hiring Manager Eleven – The following nine are most important for me;

  • CV Layout
  • Exec summary – explains in a few lines what you do, what level, what function, what sort of company
  • Key strengths/achievements – a few bullet points outling arteas you are partcularly strong
  • List jobs (reverse chronological). concentrate on achievements rather than responsibilities. use numbers – budgets, size of teams etc
  • Interests – be careful what you put!
  • Families/kids details – NO! Will that help you get the job?
  • Education/quals – leave at the end.
  • Cut out waffle – isn’t everyone energetic, passionate etc etc?

Hiring Manager Twelve – From my perspective, the information on a CV needs to be easily accessible. Very few people will read your CV in its entirety, but will be seeking certain elements as a starting point when deciding whether to engage with you or not.

As such a CV should be laid out in a predictable, chronological manner with bullet pointed responsibilities and achievements in each post clearly laid out.

A CV does not also need to be one page, particular not unless you happen to be a new graduate. Don’t clutter your CV by trying to cram it into a short space. The most controversial of the CV debates is whether or not to include a photo. I am firmly in the yes camp and would go further and include a video if well made. This transforms the CV from black and white text into something with humanity, which makes it much harder to summarily dismiss.

Hiring Manager Thirteen – First and foremost a cv needs to get passed the recruitment softwares automated screening process and / or be picked up in a recruiters search. Great candidates cv’s have all the right keywords that a recruiter would search for.

Advice you could mention: candidates should read multiple job description of suitable roles from the companies that they want to apply to. Modify their cv to suit with those same keywords.

Hiring Manager Fourteen – I always tell my candidates to make the first page of the CV stand out. Think about what the employer will be looking for and ensure that those areas of your experience and/or skill set are highlighted early on in the CV.

You never know how much time someone will actually put into reading a CV and you don’t want to be overlooked because something critical is hidden on the last page. Obviously this is general advice for job seekers. If I am representing a candidate my consultant comments will highlight the key selling points for them.

Hiring Manager Fifteen – The following five points are most important for me;

  1. It’s okay to have a “standard” resume, but when you are applying for a specific position, take the time to customize your resume so that you are highlighting the experience you have that is relevant to the requirements of the position.
  2. If you have more than ten years of experience, it’s okay to have a resume that is longer than one page, but try to keep it to two. You can always have a longer version to share once you’ve reached the interview stage. Provide more detail for recent jobs and less detail for earlier ones.
  3. If you are a manager, be sure to specify how many people you have managed, and at what levels.
  4. Wherever possible, provide examples of ways you have saved the company money or time.
  5. Finally, be CERTAIN to have someone else proofread your resume; you always need another set of eyes to catch mistakes.

Consequences of Pandemics

Consequences of Pandemics

Health Impacts

The direct health impacts of pandemics can be catastrophic. During the Black Death, an estimated 30–50 percent of the European population perished (DeWitte 2014). More recently, the HIV/AIDS pandemic has killed more than 35 million persons since 1981 (WHO Global Health Observatory data, http://www.who.int/gho/hiv/en).

Pandemics can disproportionately affect younger, more economically active segments of the population (Charu and others 2011). During influenza pandemics (as opposed to seasonal outbreaks of influenza), the morbidity and mortality age distributions shift to younger populations, because younger people have lower immunity than older people, which significantly increases the years of life lost (Viboud and others 2010). Furthermore, many infectious diseases can have chronic effects, which can become more common or widespread in the case of a pandemic. For example, Zika-associated microcephaly has lifelong impacts on health and well-being.

The indirect health impacts of pandemics can increase morbidity and mortality further. Drivers of indirect health impacts include diversion or depletion of resources to provide routine care and decreased access to routine care resulting from an inability to travel, fear, or other factors. Additionally, fear can lead to an upsurge of the “worried well” seeking unnecessary care, further burdening the health care system (Falcone and Detty 2015).

During the 2014 West Africa Ebola epidemic, lack of routine care for malaria, HIV/AIDS, and tuberculosis led to an estimated 10,600 additional deaths in Guinea, Liberia, and Sierra Leone (Parpia and others 2016). This indirect death toll nearly equaled the 11,300 deaths directly caused by Ebola in those countries (WHO 2016a). Additionally, diversion of funds, medical resources, and personnel led to a 30 percent decrease in routine childhood immunization rates in affected countries (UNDP 2014). During the 2009 influenza pandemic, a greater surge in hospital admissions for influenza and pneumonia was associated with statistically significant increases in deaths attributable to acute myocardial infarction and stroke (Rubinson and others 2013). However, during a pandemic, distinguishing which deaths are attributable to the pandemic itself and which are merely coincidental may be impossible.

During the 2014 West Africa Ebola epidemic, facilities closures as a result of understaffing and fear of contracting the disease played a large role in lack of access to or avoidance of routine health care. One study of 45 public facilities in Guinea found that the Ebola outbreak led to a 31 percent decrease in outpatient visits for routine maternal and child health services (Barden-O’Fallon and others 2015). Among children under age five years, hospitals witnessed a 60 percent decrease in visits for diarrhea and a 58 percent decrease in visits for acute respiratory illness (ARI), while health centers saw a 25 percent decrease in visits for diarrhea and a 23 percent decrease in visits for ARI. In Sierra Leone, visits to public facilities for reproductive health care fell by as much as 40 percent during the outbreak (UNDP 2014).

The availability of health care workers also decreases during a pandemic because of illness, deaths, and fear-driven absenteeism. Viral hemorrhagic fevers such as Ebola take an especially severe toll on health care workers, who face significant exposure to infectious material:

  • During the first Ebola outbreak in the Democratic Republic of Congo in 1976 (then called Zaire), the Yambuku Mission Hospital—at the epicenter of the outbreak—was closed because 11 out of the 17 staff members had died of the disease (WHO 1978).
  • During the Kikwit Ebola outbreak in 1995 in the same country, 24 percent of cases occurred among known or possible health care workers (Rosello and others 2015).
  • During the 2014 West Africa Ebola epidemic, health care workers experienced high mortality rates: 8 percent of doctors, nurses, and midwives succumbed to Ebola in Liberia, 7 percent in Sierra Leone, and 1 percent in Guinea (Evans, Goldstein, and Popova 2015).

Even if health care workers do not die, their ability to provide care may be reduced. At the peak of a severe influenza pandemic, up to 40 percent of health care workers might be unable to report for duty because they are ill themselves, need to care for ill family members, need to care for children because of school closures, or are afraid (Falcone and Detty 2015; U.S. Homeland Security Council 2006).

Economic Impacts

Pandemics can cause acute, short-term fiscal shocks as well as longer-term damage to economic growth. Early-phase public health efforts to contain or limit outbreaks (such as tracing contacts, implementing quarantines, and isolating infectious cases) entail significant human resource and staffing costs (Achonu, Laporte, and Gardam 2005). As an outbreak grows, new facilities may need to be constructed to manage additional infectious cases; this, along with increasing demand for consumables (medical supplies, personal protective equipment, and drugs) can greatly increase health system expenditures (Herstein and others 2016).

Diminished tax revenues may exacerbate fiscal stresses caused by increased expenditures, especially in LMICs, where tax systems are weaker and government fiscal constraints are more severe. This dynamic was visible during the 2014 West Africa Ebola epidemic in Liberia: while response costs surged, economic activity slowed, and quarantines and curfews reduced government capacity to collect revenue (World Bank 2014).

During a mild or moderate pandemic, unaffected HICs can offset fiscal shocks by providing increased official development assistance (ODA) to affected countries, including direct budgetary support. However, during a severe pandemic where HICs confront the same fiscal stresses and may be unable or unwilling to provide assistance, LMICs could face larger budget shortfalls, potentially leading to weakened public health response or cuts in other government spending.

The direct fiscal impacts of pandemics generally are small, however, relative to the indirect damage to economic activity and growth. Negative economic growth shocks are driven directly by labor force reductions caused by sickness and mortality and indirectly by fear-induced behavioral changes. Fear manifests itself through multiple behavioral changes. As an analysis of the economic impacts of the 2014 West Africa Ebola epidemic noted, “Fear of association with others . . . reduces labor force participation, closes places of employment, disrupts transportation, motivates some governments to close land borders and restrict entry of citizens from affected countries, and motivates private decision makers to disrupt trade, travel, and commerce by canceling scheduled commercial flights and reducing shipping and cargo services” (World Bank 2014). These effects reduce labor force participation over and above the pandemic’s direct morbidity and mortality effects and constrict local and regional trade.

The indirect economic impact of pandemics has been quantified primarily through computable general equilibrium simulations; the empirical literature is less developed. World Bank economic simulations indicate that a severe pandemic could reduce world gross domestic product (GDP) by roughly 5 percent (Burns, Van der Mensbrugghe, and Timmer 2006). The reduction in demand caused by aversive behavior (such as the avoidance of travel, restaurants, and public spaces, as well as prophylactic workplace absenteeism) exceeds the economic impact of direct morbidity- and mortality-associated absenteeism.

These results align with country-specific estimates: an analysis of pandemic influenza’s impact on the United Kingdom found that a low-severity pandemic could reduce GDP by up to 1 percent, whereas a high-severity event could reduce GDP by 3–4 percent (Smith and others 2009). The World Bank’s estimates from the 2014 West Africa Ebola epidemic suggest that economic disruption in low-income countries (LICs) could be even greater. For example, the 2015 economic growth estimate for Liberia was 3 percent (against a pre-Ebola estimate of 6.8 percent); for Sierra Leone, it was −2 percent (against a pre-Ebola estimate of nearly 9 percent) (Thomas and others 2015).

Finally, estimates of fiscal and growth shocks are significant but do not include the intrinsic value of lives lost. Fan, Jamison, and Summers () consider this additional dimension of economic loss by estimating the value of excess deaths across varying levels of modeled pandemic severity, finding that the bulk of the expected annual loss from pandemics is driven by the direct cost of mortality, particularly in the case of low-probability, severe events.

During a severe pandemic, all sectors of the economy—agriculture, manufacturing, services—face disruption, potentially leading to shortages, rapid price increases for staple goods, and economic stresses for households, private firms, and governments. A sustained, severe pandemic on the scale of the 1918 influenza pandemic could cause significant and lasting economic damage.

Social and Political Impacts

Evidence suggests that epidemics and pandemics can have significant social and political consequences, creating clashes between states and citizens, eroding state capacity, driving population displacement, and heightening social tension and discrimination (Price-Smith 2009).

Severe premodern pandemics have been associated with significant social and political upheaval, driven by large mortality shocks and the resulting demographic shifts. Most notably, deaths arising from the introduction of smallpox and other diseases to the Americas led directly to the collapse of many indigenous societies and weakened the indigenous peoples’ institutions and military capacity to the extent that they became vulnerable to European conquest (Diamond 2009; see table 17.1). Subsequent pandemics have not had such dramatic effects on political and social stability, primarily because the potential mortality shock has been attenuated by improvements in prevention and care.

Evidence does suggest that epidemics and pandemics can amplify existing political tensions and spark unrest, particularly in fragile states with legacies of violence and weak institutions. During the 2014 West Africa Ebola epidemic, steps taken to mitigate disease transmission, such as the imposition of quarantines and curfews by security forces, were viewed with suspicion by segments of the public and opposition political leaders. This led directly to riots and violent clashes with security forces (). Latent political tensions from previously warring factions in Liberia also reemerged early in the epidemic and were linked with threats to health care workers as well as attacks on public health personnel and facilities.

The Ebola epidemic also greatly amplified political tensions in Guinea, Liberia, and Sierra Leone, with incumbent politicians accused of leveraging the crisis and disease control measures to cement political control and opposition figures accused of hampering disease response efforts (ICG 2015). Whereas growing tensions did not lead to large-scale political violence or instability, they did complicate public health response efforts. In Sierra Leone, quarantine in opposition-dominated regions was delayed because of concerns that it would be seen as politically motivated (ICG 2015). In countries with high levels of political polarization, recent civil war, or weak institutions, sustained outbreaks could lead to more sustained and challenging political tensions.

Pandemics also can have longer-term impacts on state capacity (Price-Smith 2001). The HIV/AIDS pandemic offers one notable example. The 1990s and early 2000s saw extremely high HIV/AIDS prevalence rates among African militaries, leading to increased absenteeism, decreased military capacity, and decreased readiness (Elbe 2002). Similar effects may occur during shorter, more acute pandemics, reducing state capacity to manage instability. The weakening of security forces can, in turn, amplify the risk of civil war and other forms of violent conflict (Fearon and Laitin 2003).

Large-scale outbreaks of infectious disease have direct and consequential social impacts. For example, widespread public panic during disease outbreaks can lead to rapid population migration. A 1994 outbreak of plague in Surat, India, caused only a small number of reported cases, but fear led some 500,000 people (roughly 20 percent of the city’s population, including a disproportionately large number of clinicians) to flee their homes (Barrett and Brown 2008). Sudden population movements can have destabilizing effects, and migrants face elevated health risks arising from poor sanitation, poor nutrition, and other stressors (Toole and Waldman 1990). Migration also poses the risk of further spreading an outbreak.

Finally, outbreaks of infectious disease can cause already vulnerable social groups, such as ethnic minority populations, to be stigmatized and blamed for the disease and its consequences (Person and others 2004). During the Black Death, Jewish communities in Europe faced discrimination, including expulsion and communal violence, because of stigma and blame for disease outbreaks (Cohn 2007). Modern outbreaks have seen more subtle forms of discrimination, such as shunning and fear, directed at minority populations linked with disease foci. For example, Africans in Hong Kong SAR, China, reported experiencing social isolation, anxiety, and economic hardship resulting from fears of their association with Ebola (Siu 2015).

Burden of Pandemics

Burden of Pandemics

Quantifying the morbidity and mortality burden from pandemics poses a significant challenge. Although estimates are available from historical events, the historical record is sparse and incomplete. To overcome these gaps in estimating the frequency and severity of pandemics, probabilistic modeling techniques can augment the historical record with a large catalog of hypothetical, scientifically plausible, simulated pandemics that represent a wide range of possible scenarios. Modeling can also better account for changes that have occurred since historical times, such as medical advances, changing demographics, and shifting travel patterns.

Scenario modeling of epidemics and pandemics can be achieved through large-scale computer simulations of global spread, dynamics, and illness outcomes of disease (Colizza and others 2007; Tizzoni and others 2012). These models allow for specification of parameters that may drive the likelihood of a spark (for example, location and frequency) and determinants of severity (for example, transmissibility and virulence). The models then simulate at a daily time step the spread of disease from person to person via disease transmission dynamics and from place to place via incorporation of long-range and short-range population movements. The models also can incorporate mitigation measures, seasonality, stochastic processes, and other factors that can vary during an epidemic. Millions of these simulations can be run with wide variation in the initial conditions and final outcomes.

These millions of simulations can be used to quantify the burden of pandemics through a class of probabilistic modeling called catastrophe modeling, which the insurance industry uses to understand risks posed by infrequent natural disasters such as hurricanes and earthquakes (Fullam and Madhav 2015; Kozlowski and Mathewson 1997). When applied to pandemics, this approach requires statistically fitting distributions of the parameters. These parameter distributions provide weightings of the likelihood of the different events. Through correlated statistical sampling based on the parameter weights, scenarios are selected for inclusion in an event catalog of simulated pandemic events. A schematic diagram shows how the catastrophe modeling process is used to develop the event catalog.

Analysis of the event catalog yields annual EP curves (for example, as shown in figure 17.2), which provide a metric of the likelihood that an event of a given severity, or worse, begins in any given year. The EP curve is a visualization of the event catalog, in which the number of estimated deaths for each event is ranked in descending order. Because the event catalog includes scenarios incorporating spark probabilities and estimates of disease propagation, the EP curve includes the combined impacts of both spark risk and spread risk. Although a global curve is shown in figure 17.2, EP curves can be estimated for other geographic resolutions, such as a country or province.


Estimated Annual Exceedance Probability Curve for Global Pneumonia and Influenza Deaths Caused by Influenza Pandemics, 2017.

The EP curve is a powerful tool that yields several key findings regarding the frequency and severity of potential pandemics. Applied to influenza pandemics, we find the following:

  • An influenza pandemic having the global mortality rate observed during the 2009 Swine flu pandemic (0.2–0.8 deaths per 10,000 persons) or worse has about a 3 percent probability of occurring in any given year.
  • In any given year, the probability of an influenza pandemic causing nearly 6 million pneumonia and influenza deaths (8 deaths per 10,000 persons) or more globally is 1 percent.
  • The annual probability of an influenza pandemic’s meeting or exceeding the global mortality rate of the 1918 Spanish flu pandemic (111–555 deaths per 10,000 persons) is less than 0.02 percent.
  • As indicated by the heavy tail of the EP curve, most of the potential burden from influenza pandemics comes from the most severe pandemics.

Table 17.4 shows select EPs for influenza pandemics in low-, middle-, and high-income countries, based on further analysis of the event catalog. For example, in any given year, all LICs combined have a 3 percent probability of experiencing at least 140,000 deaths attributable to an influenza pandemic and a 0.1 percent chance of experiencing at least 8.3 million deaths. LICs bear a substantial burden of mortality risk from influenza pandemics. Strikingly, LICs contain only about 9 percent of the global population, yet they would contribute nearly 25 percent of deaths during an influenza pandemic.

Based on the event catalog, the average estimated global mortality from pneumonia and influenza during an influenza pandemic is more than 7.3 million deaths. However, because influenza pandemics occur on average once every 25–30 years, the average annual pneumonia and influenza mortality from influenza pandemics is a little more than 230,000 deaths. This is comparable to seasonal influenza, which worldwide causes at least 250,000 deaths annually (WHO 2016b). Although both numbers reflect an annual average, they differ in the combination of frequency and severity. Seasonal influenza deaths occur every year, but pandemic influenza deaths occur much less frequently, are concentrated in larger spikes, and affect a younger demographic.

When pandemics cause large morbidity and mortality spikes, they are much more likely to overwhelm health systems. Overwhelmed health systems and other indirect effects may contribute to a 2.3-fold increase in all-cause mortality during pandemics, although attribution of the causative agent is difficult (Simonsen and others 2013). If indirect deaths are taken into account, the average annual global deaths from influenza pandemics could be greater than 520,000, although there is a significant uncertainty in the estimate.

Pandemics caused by pathogens other than influenza also must be considered. Novel coronaviruses (such as SARS-CoV), filoviruses (such as Ebola virus), and flaviviruses (such as Zika virus) have caused large epidemics and pandemics. These viruses, like influenza, are ribonucleic acid viruses that have high mutation rates. Noninfluenza viruses typically cause more frequent, smaller epidemics but also an overall lower burden of morbidity and mortality than pandemic influenza. For diseases caused by coronaviruses and filoviruses, the lower burden stems from the mode of transmission, which often requires closer and more sustained contact than influenza does to spread.

Trends Affecting Pandemic Risk

Trends Affecting Pandemic Risk

In recent decades, several trends have affected pandemic probability, preparedness, and mitigation capacity. Various factors—population growth, increasing urbanization, greater demand for animal protein, greater travel and connectivity between population centers, habitat loss, climate change, and increased interactions at the human-animal interface—affect the likelihood of pandemic events by increasing either the probability of a spark event or the potential spread of a pathogen (Tilman and Clark 2014; Tyler 2016; Zell 2004). With global population estimated to reach 9.7 billion by 2050 and with travel and trade steadily intensifying, public health systems will have less time to detect and contain a pandemic before it spreads (Tyler 2016).

As for poverty, the trends are mixed. On the positive side, enormous gains in poverty reduction have decreased the number of people living in extreme poverty. This may attenuate the mortality shock of a mild pandemic somewhat. On the negative side, extreme poverty is now concentrated in a small number of low-growth, high-poverty countries (Chandy, Kato, and Kharas 2015). In such countries, progress in building health system capacity also has been far slower.

Likewise, for a subset of countries with endemically weak institutions, building institutional capacity for complex tasks like pandemic mitigation and response is likely to be a slow process even under the most optimistic assumptions (Pritchett, Woolcock, and Andrews 2013). Many of these countries are in areas with high spark risk, particularly in Central and West Africa, and thus may remain vulnerable and require significant international assistance during a pandemic.

Other environmental and population trends that could increase the severity of pandemics include the persistence of slums, unresponsive health systems, higher prevalence of comorbidities, weaker sanitation, and aging populations (Arimah 2010; UNDESA 2015). The increasing threat posed by antibiotic resistance also could amplify mortality during pandemics of bacterial diseases such as tuberculosis and cholera and even viral diseases (especially for influenza, in which a significant proportion of deaths is often the result of bacterial pneumonia coinfections) (Brundage and Shanks 2008; Van Boeckel and others 2014).

Pandemic intervention costs and cost-effectiveness

Innovations in pandemic financing have been developed in response to the significant burden that a pandemic can place on a country’s financial resources. One such innovation is the World Bank’s Pandemic Emergency Financing Facility (PEF) (Katz and Seifman 2016). A type of disaster risk pool, the PEF provides poorly resourced countries with an infusion of funds to help with the costs of response in the early stages of an epidemic or pandemic. The maximum total coverage over a three-year period is US$500 million. Notably, the US$500 million coverage is much lower than the estimated US$3.8 billion cost of the multinational response to the 2014 West Africa Ebola epidemic (USAID and CDC 2016). Because the PEF is designed to trigger early in an outbreak, the anticipated funding is less than would be required for a full-fledged response once a widespread pandemic is under way.

Risk transfer mechanisms such as insurance offer an injection of financial resources to help insured parties rapidly scale up disease response activities. As such, the utility of risk transfer mechanisms depends, in large part, on the absorptive capacity of the insured party. A country must have the ability to use insurance payouts effectively to access additional human resources (clinicians, community health workers), personal protective equipment and other medical equipment consumables, and vaccines and therapeutics, from either domestic or international resources.

Adequacy of Evidence on Pandemics in LMICs

Much of the available data regarding pandemics (including the morbidity and mortality impacts of historical pandemics) and the effectiveness of different preparedness efforts and interventions come from HICs and upper-middle-income countries. Understanding of the prevalence of risk drivers, especially regarding spark risk, has improved markedly in both high- and low-income contexts. However, gaps in surveillance and reporting infrastructure in LMICs mean that, during a pandemic, many cases may never be detected or reported to the appropriate authorities (Katz and others 2012). Particularly in LICs, empirical data on outbreak occurrences may be biased downward systematically.

Additionally, the means to disseminate collected data rapidly may not exist. For example, data may be kept in paper archives, so resource-intensive digitization may be required to analyze and report data to a wider audience. Data dissemination challenges are further compounded by a publication bias that results in over-representation of HICs in the scientific literature (Jones and others 2008).


Few data are available regarding costs and cost-effectiveness of pandemic preparedness and response measures, and they focus almost exclusively on HICs. The available data suggest that the greatest cost-related benefits in pandemic preparedness and response are realized from early recognition and mitigation of disease—that is, catching and stopping sparks before they spread. Costs can be reduced if action is taken before an outbreak becomes a pandemic. Similarly, once a pandemic has begun, preventing illness generally is more cost-effective than treating illness, especially because hospitalizations typically have the highest direct cost per person. High costs also may occur as a result of interventions (such as quarantines and school closures) that lead to economic disruption. These interventions may be more cost-effective during a severe pandemic.

Program and Health System Costs

No systematic time-series data exist on global spending on pandemic preparedness, and arriving at an exact figure is complicated by the fact that many investments in building basic health system capacity also support core dimensions of pandemic preparedness. An analysis of global health spending found that roughly 1 percent of global ODA spending on health in 2013 (approximately US$204 million) focused specifically on pandemic preparedness (Schäferhoff and others 2015). Other, non-ODA spending on pandemic preparedness is similarly difficult to measure but likely to be significant; in 2013, the U.S. Department of Defense spent roughly US$256 million on efforts to build global biosurveillance and response capacities (KFF 2014).

Globally, the current funding for pandemic preparedness and response falls short of what is needed. In 2016, the international Commission on a Global Health Risk Framework for the Future recommended an additional US$4.5 billion annual global investment for upgrading pandemic preparedness at the country level, for funding infectious disease research and development efforts, and for establishing or replenishing rapid-response financing mechanisms such as the World Bank’s PEF (Sands, Mundaca-Shah, and Dzau 2016).

Costs for efforts associated with prepandemic preparedness activities also are not well quantified, although investment in One Health activities is likely to be cost-effective (World Bank 2012). The USAID PREDICT project has estimated that discovery and detection of the majority of zoonotic viruses would cost US$1.6 billion (Anthony and others 2013). The Global Virome Project, a more comprehensive study aiming to characterize more than 99 percent of the world’s viruses, is estimated to cost US$3.4 billion over 10 years (Daszak and others 2016). Building on efforts to identify and describe the ecology of potential pandemic viruses, the Coalition for Epidemic Preparedness Innovations (CEPI) estimated a cost of US$1 billion over five years to develop vaccine candidates against known emerging infectious diseases (for example, Ebola virus) and to build technology platforms and production facilities to accelerate vaccine response to outbreaks of known or unknown pathogens (Brende and others 2017).

Instituting response measures after a pandemic has begun can be expensive, with most of the direct cost borne by the health care sector, although response costs typically are not reported in a cohesive manner. As noted, the response to the 2014 West Africa Ebola epidemic cost more than US$3.8 billion, including donations from several countries (USAID and CDC 2016). Additionally, the World Bank Group mobilized US$1.6 billion from the International Development Association and the International Finance Corporation to stimulate economic recovery in the three worst-affected countries of Guinea, Liberia, and Sierra Leone (World Bank 2016). Taken together, at US$5.4 billion, these values amount to a cost of US$235 per capita for these three countries.

When total costs for response are not available, unit costs for response activities provide valuable insights. Figure 17.4 shows estimated unit costs for selected response measures, based on modeling studies for pandemic influenza in HICs. Vaccinations and medicines have the lowest unit costs; in LMICs, large-scale purchasing and subsidies could push drug costs down even more. Conversely, hospital care has the highest unit costs. Costs per day of hospitalization (especially those with ICU involvement) can add up quickly when aggregated at the national level. However, these medical care costs are potentially bounded by capacity limits (such as a finite number of hospital beds), especially during more severe pandemics.

Pandemic severity itself can play a role in the drivers of cost and the effects of mitigation efforts. One study based on modeling simulations in an Australian population found that, in low-severity pandemics, most costs borne by the larger economy (not just the health care system) come from productivity losses related to illness and social distancing. In higher-severity pandemics, the largest drivers of costs are hospitalization costs and productivity loss because of deaths (Milne, Halder, and Kelso 2013).

Costs per Death Prevented

Figure 17.5 depicts a compilation of data from 18 scientific publications that examined costs and benefits associated with response during the 2009 influenza pandemic. The lowest costs per deaths prevented were found for contact tracing, face masks, and surveillance. Pharmaceutical interventions such as vaccines and antiviral therapies were in the midrange.

Measures that decreased person-to-person contact, including social distancing, quarantine, and school closures, had the greatest cost per death prevented, most likely because of the amount of economic disruption caused by those measures. Social distancing includes avoidance of large gatherings and public places where economic activities occur. School closures often lead to lost productivity because they cause workplace absenteeism among caretakers of school-age children. Macroeconomic model simulations also have identified school closures as a potential source of GDP loss during a moderately severe pandemic (Smith and others 2009).

The information shown in figure 17.5 is subject to several caveats:

  • The data come from only a few studies covering a handful of countries.
  • Cost-utility analyses of pandemic preparedness and response for LMICs are rare. Because the underlying data for these studies were drawn primarily from HICs, the estimates may not accurately represent the relative benefit-cost of interventions in LMICs. For example, in countries with high unemployment and underemployment, school closures may not lead to increased workforce absenteeism and thus might have a lower cost per death prevented.
  • The 2009 influenza pandemic is considered a relatively mild pandemic. In a more severe influenza pandemic, the cost per death prevented could decrease for some interventions, such as school closures.
  • Results are sensitive to assumptions about the value of a prevented death and estimated costs of different interventions.
  • The data cover only pandemics caused by influenza. For pandemics caused by other types of pathogens, the cost-utility values may be different, and not all intervention measures may be available.

Data on antiviral stockpiles provide some insight into how the cost utility of pandemic preparedness efforts may vary by country income level. Figure 17.6 shows the cost utility of antiviral stockpiling by country income level, based on simulation studies.

A more recent study found that antiviral stockpiling in Cambodia (a lower-middle-income country) would cost between US$3,584 and US$115,168 per death prevented; however, this result is highly sensitive to assumptions about the timing between pandemics (Drake, Chalabi, and Coker 2015).

Although based only on a handful of countries, the results suggest that antiviral stockpiling in LICs has an extremely high cost per death prevented, whereas countries at other income levels are clustered within much lower ranges. Antiviral stockpiling is not cost-effective or feasible for LICs, primarily because of the high cost of antiviral agents. For stockpiling to be a cost-effective strategy for LICs, almost all of the costs would have to be subsidized. The associated costs also may be reduced by the increased availability of generic antiviral drugs. Additionally, the efficacy of antivirals is not assured, particularly for LICs, which may not be able to identify cases early enough to administer antivirals efficaciously.


Pérez Velasco and others (2012) synthesized information from 44 studies that contained economic evaluations of influenza pandemic preparedness and response strategies in HICs (figure 17.7). In their analysis, the following interventions among the general population had the potential to provide cost savings: vaccines, antiviral treatment, social distancing, antiviral prophylaxis plus antiviral treatment, and vaccines plus antiviral treatment. The cost savings from antiviral drugs found in this study are likely to be diminished in LMICs, as inability to deploy antivirals in a timely manner poses a serious challenge to their efficacious use.

Depending on the characteristics of a pandemic and how mitigation efforts are implemented, some mitigation strategies could become highly cost-ineffective. For example, a costly vaccination campaign that is carried out in an area well after a pandemic peaks is not nearly as effective in reducing transmission as having vaccines available and distributed earlier in the pandemic.

Allocation of limited resources (by creating priority groups for vaccines and antivirals) is an important consideration during a pandemic. Modeling studies from the 2009 influenza pandemic investigated the most cost-effective strategies for allocating vaccines. Those studies found that vaccinating high-risk individuals was more cost-effective than prioritizing children. Favoring children decreased the overall infection rate, but high-risk individuals were the predominant drivers of direct costs during the pandemic, because they were more likely to be hospitalized (Lee and others 2010). However, these studies did not account for the indirect costs of school closures and absenteeism. Consideration of these factors could reveal increased cost savings from vaccinating children.

Another key question for benefit-cost analyses related to pandemics is the extent to which stockpiles of vaccines, antiviral drugs, and protective equipment should be assembled in advance of a pandemic. Vaccines for a novel influenza virus can take several months to develop, and vaccines for other pathogens (for example, Ebola and Zika) can take even longer to develop. Studies have examined the cost-effectiveness of stockpiling prepandemic vaccines that have lower efficacy than reactive vaccines but can be deployed more quickly. One study found that cost savings can be obtained as long as prepandemic vaccines have at least 30 percent efficacy. However, cost-effectiveness differs by pandemic severity and the percentage of the population that receives the vaccine during the vaccination campaign (Halder, Kelso, and Milne 2014).

Antiviral drugs to fight pandemic influenza also can be stockpiled ahead of time. However, the optimal number of doses to stockpile depends on factors including the effectiveness of concurrent interventions and the likelihood of antiviral wastage on noninfluenza respiratory infections (Greer and Schanzer 2013).

Most pandemic-related benefit-cost studies focus on pharmaceutical interventions for high-income and upper-middle-income countries. The studies have largely neglected the question of how to allocate strained resources in low- and lower-middle-income countries. Furthermore, few evaluations have been conducted of the cost-effectiveness of general investment in health systems, infrastructure, and capacity building as a means to achieve pandemic preparedness (Drake, Chalabi, and Coker 2012)

Pandemic risk factors

Pandemic risk, as noted, is driven by the combined effects of spark risk and spread risk. The foci of both risk factors often overlap, especially in some LMICs (such as in Central and West Africa and Southeast Asia), making these areas particularly vulnerable to pandemics and their negative consequences.

Spark Risk

A zoonotic spark could arise from the introduction of a pathogen from either domesticated animals or wildlife. Zoonoses from domesticated animals are concentrated in areas with dense livestock production systems, including areas of China, India, Japan, the United States, and Western Europe. Key drivers for spark risk from domesticated animals include intensive and extensive farming and livestock production systems and live animal markets, as well as the potential for contact between livestock and wildlife reservoirs (Gilbert and others 2014Jones and others 2008).

Wildlife zoonosis risk is distributed far more broadly, with foci in China, India, West and Central Africa, and the Amazon Basin (Jones and others 2008). Risk drivers include behavioral factors (such as bushmeat hunting and use of animal-based traditional medicines), natural resource extraction (such as sylviculture and logging), the extension of roads into wildlife habitats, and environmental factors (including the degree and distribution of animal diversity) (Wolfe and others 2005).

Spread Risk

After a spark or importation, the risk that a pathogen will spread within a population is influenced by pathogen-specific factors (including genetic adaptation and mode of transmission) and human population-level factors (such as the density of the population and the susceptibility to infection; patterns of movement driven by travel, trade, and migration; and speed and effectiveness of public health surveillance and response measures) (Sands and others 2016).

Dense concentrations of population, especially in urban centers harboring overcrowded informal settlements, can act as foci for disease transmission and accelerate the spread of pathogens (Neiderud 2015). Moreover, social inequality, poverty, and their environmental correlates can increase individual susceptibility to infection significantly (Farmer 1996). Comorbidities, malnutrition, and caloric deficits weaken an individual’s immune system, while environmental factors such as lack of clean water and adequate sanitation amplify transmission rates and increase morbidity and mortality (Toole and Waldman 1990). Collectively, all these factors suggest that marginalized populations, including refugees and people living in urban slums and informal settlements, likely face elevated risks of morbidity and mortality during a pandemic.

A country’s expected ability to curtail pandemic spread can be expressed using a preparedness index developed by Oppenheim and others (2017). The index illustrates global variation in institutional readiness to detect and respond to a large-scale outbreak of infectious disease. It draws on the IHR core capacity metrics and other publicly accessible cross-national indicators. However, it diverges from the IHR metrics in its breadth and focus on measuring underlying and enabling institutional, infrastructural, and financial capacities such as the following (Oppenheim and others 2017):

  • Public health infrastructure capable of identifying, tracing, managing, and treating cases
  • Adequate physical and communications infrastructure to channel information and resources
  • Fundamental bureaucratic and public management capacities
  • Capacity to mobilize financial resources to pay for disease response and weather the economic shock of the outbreak
  • Ability to undertake effective risk communications.

Well-prepared countries have effective public institutions, strong economies, and adequate investment in the health sector. They have built specific competencies critical to detecting and managing disease outbreaks, including surveillance, mass vaccination, and risk communications. Poorly prepared countries may suffer from political instability, weak public administration, inadequate resources for public health, and gaps in fundamental outbreak detection and response systems.

Map 17.1 presents the global distribution of epidemic preparedness, with countries grouped into quintiles. A geographic analysis of preparedness shows that some areas of high spark risk also are the least prepared. Geographic areas with high spark risk from domesticated animals (including China, North America, and Western Europe) have relatively higher levels of preparedness, although China lags behind its counterparts. However, geographic areas with high spark risk from wildlife species (including Central and West Africa) have some of the lowest preparedness scores globally, indicating a potentially dangerous overlap of spark risk and spread risk.

Care and treatment to reduce the severity of pandemic illness

Care and Treatment to Reduce the Severity of Pandemic Illness

During a pandemic, health authorities work to reduce the severity of illness through patient care and treatment, which can help decrease the likelihood of severe outcomes such as hospitalizations and deaths. Treatments may range from nonspecific, supportive care to disease-specific drugs. During the prepandemic period, plans to implement these measures should be developed and tested through simulation exercises.

Maintaining supportive care during an epidemic or pandemic can improve mortality rates by alleviating the symptoms of disease. During the 2014 West Africa Ebola epidemic, for example, evidence suggests that earlier case identification, supportive care, and rehydration therapy modestly reduced mortality (Walker and Whitty 2015). Indeed, despite the unavailability of antivirals or vaccines, efforts to engage communities with added medical supplies and trained clinicians decreased the case-fatality ratio moderately as more patients trusted, sought, and received clinical care (Aylward and others 2014). Medical supplies that may be needed for supportive care during a pandemic include hospital beds, disinfectants, ICU supplies (such as ventilators), and personal protective equipment (WHO 2015b).

Medical interventions for pandemic influenza include antiviral drugs and antibiotics to treat bacterial coinfections. Antivirals especially may reduce mortality when given within 48 hours of symptom onset (Domínguez-Cherit and others 2009; Jain and others 2009). However, because of delays in case identification and antiviral deployment (as discussed in box 17.2), LMICs may experience only limited benefits from antiviral drugs.

Potential for Scaling Up

The term scaling up refers to the expansion of health intervention coverage (Mangham and Hanson 2010). In the context of pandemic preparedness, successfully scaling up requires health systems to expand services to accommodate rapid increases in the number of suspected cases. Scaling up is facilitated by surge capacity (the ability to draw on additional clinical personnel, logisticians, and financial and other resources) as well as preexisting operational relationships and plans linking government, nongovernmental organizations, and the private sector. Ultimately, scaling up consists of having both local surge capacity and the absorptive capacity to accept outside assistance.

Local capacity building is vital, and some capacities may have particularly important positive externalities during outbreaks. During the 2014 Ebola importation into Nigeria, surge capacity that existed because of polio eradication efforts contributed to a more successful outbreak response (Yehualashet and others 2016). Key elements included national experience running an emergency operations center and the use of global positioning systems to support contact tracing (Shuaib and others 2014; WHO 2015a).

Stockpiling of vaccines, medicines (including antibiotics and antivirals), and equipment (such as masks, gowns, and ventilators) also can be useful for building local surge capacity (Dimitrov and others 2011; Jennings and others 2008; Morens, Taubenberger, and Fauci 2008; Radonovich and others 2009). During a pandemic, health systems can tap into stockpiles more quickly than they can procure supplies from external sources or boost production. However, there are five important considerations for keeping stockpiles:

  • Building a stockpile requires significant up-front costs, which can be especially prohibitive for LICs (Oshitani, Kamigaki, and Suzuki 2008).
  • Prepandemic vaccines may not be closely matched to the pathogen causing the pandemic.
  • The optimal size of a stockpile can be challenging to determine.
  • Stockpiles need to be refreshed regularly, because pharmaceuticals and equipment can reach expiration dates.
  • Robust health systems and channels for disseminating and using the stockpiles also must exist.

Boosting local production capacity for necessary supplies may be a viable strategy for pandemic preparedness and may circumvent some of the challenges associated with amassing stockpiles.

The 2009 influenza pandemic demonstrated how scaling up can affect the success rate of a mass vaccination campaign (table 17.5). Vaccination rates increased according to country income level, suggesting that vaccination campaigns were most successful in HICs, likely because of the size of their stockpiles, increased manufacturing capacity for vaccines, increased availability of vaccines, and more streamlined logistics in vaccine deployment.

Building local capacity to scale up is challenging, especially in LMICs. The biggest challenges include infrastructural gaps (such as weak road, transportation, and communications networks) and shortfalls in human resources (such as logisticians, epidemiologists, and clinical staff). Bilateral and multilateral aid organizations have channeled substantial funding into building and sustaining local technical capacities in LMICs. This type of investment is critically important. But, particularly in LMICs with weak health system capacity, progress in expanding local surge capacity likely will be slow.

Another key component of scaling up, especially in LMICs, is the ability to use external assistance effectively. During the 2014 West Africa Ebola epidemic, a surge of foreign clinicians, mobile medical units, and epidemiologists and other public health personnel was required to bolster limited local resources. LMICs can improve systems to facilitate and coordinate surges of foreign support in the following ways:

  • Streamline customs processes for critical medical supplies and drugs.
  • Establish mechanisms to coordinate the deployment and operations of foreign medical teams.
  • Build mechanisms to coordinate between military and humanitarian units involved in crisis response.

Even so, local absorptive capacity (that is, the ability to channel and use foreign assistance effectively) has its limits. Constraints in bureaucratic capacity, financial controls, logistics, and infrastructure all are likely to be most severe in the countries that most need foreign assistance to manage infectious disease crises.

Furthermore, although external assistance is a viable strategy during localized epidemics, it has limitations that are likely to arise during large-scale pandemics. First, supply constraints exist, including limits to the number of medical personnel (especially those with crisis response and infectious disease competencies) and the number of specialized resources (such as integrated mobile medical clinics available for deployment).

Second, during a severe pandemic, countries are likely to use such resources locally before providing medical assistance abroad. The global humanitarian system provides a critical reservoir of crisis response capacity and shock absorption. However, the humanitarian system currently is straining under the pressure of other crises, including upsurges in violent conflict (Stoddard and others 2015). A severe epidemic or pandemic can quickly outstrip international resources. Médecins Sans Frontières (Doctors Without Borders), an international health organization with deep experience providing Ebola treatment, found itself “pushed to the limits and beyond” during the 2014 West Africa Ebola epidemic (MSF 2015).

Risk Transfer Mechanisms

As with any other type of natural disaster, the risk from pandemics cannot be eliminated. Despite prevention efforts, pandemics will continue to occur and will at times overwhelm the systems that have been put in place to mitigate their health, societal, and economic effects. The residual risk may be significant, particularly for LMICs that lack the resilience or resources to absorb shocks to public health and public finances. Risk transfer mechanisms (such as specialized insurance facilities) offer an additional tool to manage this risk.

Risk-based insurance products are increasingly deployed in LMICs to pay for remediation and reconstruction costs following natural catastrophes such as hurricanes, floods, and droughts (ARC 2016; IFRC 2016). Insurance products for epidemics and pandemics require specific characteristics. First, insurance policies should be designed to release discretionary funds early in the course of an outbreak. In situations where financing poses a constraint to mobilizing personnel, drugs, or other supplies, payouts can be used to mobilize a public health response and mitigate further spread of disease, reducing the potential health and economic impacts of the pandemic. Second, because pandemics do not stay contained in national borders, a strong case can be made for mobilizing bilateral and multilateral financing of LMICs’ insurance premiums as a cost-effective way to improve global preparedness and support mitigation efforts. Third, risk transfer systems require the availability of rigorously and transparently compiled data to trigger a payout. In the context of pandemic insurance, the development of risk transfer systems requires countries to build the following capacities, among others:

  • Robust surveillance data to identify when an outbreak has reached sufficient scale to require the release of funds
  • Laboratory capacity to confirm the causative agent
  • Predefined contingency and response plans to spend the funds effectively upon their release.

Insurance facilities can create positive incentives for LMICs to invest in planning and capacity building. Insurance mechanisms may have other positive externalities: most notably, the potential release of funds may provide a strong incentive for the timely reporting of surveillance data. However, insurance facilities also may introduce perverse incentives (including incentives to distort surveillance data) and potential moral hazards (such as permitting riskier activities). These incentive problems may be mitigated in the design of the risk transfer mechanism, such as by providing coverage only when minimum requirements for surveillance accuracy are met, by having preset phased triggers for payouts, and by including incentive payouts for successfully containing an outbreak.

Relative to investments in basic health provision, building capacity in infectious disease surveillance systems and other dimensions of pandemic preparedness has uncertain and potentially distant benefits. In LICs where near-term health needs are acute, this can complicate the political and economic logic for investing in pandemic preparedness (Buckley and Pittluck 2016). The use of catastrophe modeling tools (such as EP curves) can clarify the benefit-cost rationale and the relevant time horizon for investments in preparedness, and it can inform the design and financial structure of pandemic insurance policies.

Figure 17.3 shows a country’s hypothetical pandemic preparedness budget allocation and the portion of risk transfer in estimated total costs of spread response. In this example, a country has a total budget of US$100 million to cover all aspects of pandemic preparedness during the prepandemic, spark, and spread periods. After allocating half of the funds for prepandemic and spark response activities, US$50 million is left for pandemic spread response. On the basis of its risk tolerance, the country makes a decision to manage its risk at the 3 percent annual probability point on its EP curve. Modeling estimates indicate that a successful response to a pandemic at this level would require at least US$125 million, which would fund spread response activities, shown in box 17.1. Because only US$50 million is left after allocation to prepandemic and spark response activities, this would leave a shortfall of US$75 million. Some or all of this shortfall could be offloaded to another entity, such as a catastrophe risk insurance pool, which would give the country access to a payout during a pandemic.

How to boost your self-confidence

Being a more confident person is going to help you to succeed and feel happier as you journey through life. You’ll be able to face your fears without much hesitation and meet your goals at a quicker rate when you’re not constantly questioning yourself.

First, you have to believe in yourself and know that you’re worth it.

Avoid letting your past define you or dictate how your future will play out.

Implement the following tips in your life, and you’ll likely soon find yourself feeling more confident, and that you no longer need to hide and shy away from people or new situations. Be patient and give it time because gaining more self-confidence doesn’t happen overnight.

1.   Find a Job You Love

You’ll discover that you can boost your self-esteem when you’re working a job that you’re good at and that you love at the same time. Being in a dead-end position where your employer doesn’t value your talent will take a toll on your mental health over time. You may start to question your abilities and stop trying to improve and go after what you want. Therefore, if you’re not happy in your current role, then it’s time to challenge yourself to find a new job that puts your skills to use.

2.   Take Pride in Your Appearance

You can also boost your self-confidence by taking more pride in your appearance. You should love who you see in the mirror each day and feel excited about the way you look so that you carry yourself well. For example, if you’ve been struggling to take off the weight, then there’s no better time than the present to take action. One idea is to look into a Weight Loss program and get started today to lose any unwanted pounds. You’re more likely to walk around with your head held high when you feel comfortable and confident in your body.

3.   Avoid Comparisons

Social media makes it easy to compare yourself to other people’s lives. However, your self-esteem may suffer if you’re always worrying about someone else and becoming jealous about what they have. Instead, practice gratitude for your blessings and stay focused on your own goals and achievements. Constant comparisons will steal your joy, and you’ll forget about all the wonderful qualities you have to offer. Try your best to be happy for others when you see them succeeding, and also to know that you have plenty to be proud of at the end of the day.

4.   Closely Monitor Your Self-Talk

Another way to boost your self-confidence is to monitor your self-talk closely, especially when it’s negative in nature. What you tell yourself each day will soon become your reality and what you believe. Therefore, make sure you avoid and challenge a negative inner-voice by focusing on using encouraging words that make you feel good about yourself. You have to believe in yourself before others will, so it’s essential to find ways to keep a positive mindset and not let setbacks or failures get you down.

Origin of Pandemics

Most new pandemics have originated through the “zoonotic” transmission of pathogens from animals to humans (Murphy 1998; Woolhouse and Gowtage-Sequeria 2005), and the next pandemic is likely to be a zoonosis as well.

Zoonoses enter into human populations from both domesticated animals (such as farmed swine or poultry) and wildlife. But many historically significant zoonoses were introduced through increased human-animal interaction following domestication, and potentially high-risk zoonoses (including avian influenzas) continue to emerge from livestock production systems (Van Boeckel and others 2012; Wolfe, Dunavan, and Diamond 2007).

Some pathogens (including Ebola) have emerged from wildlife reservoirs and entered into human populations through the hunting and consumption of wild species (such as bushmeat), the wild animal trade, and other contact with wildlife (Pike and others 2010; Wolfe, Dunavan, and Diamond 2007).

Zoonotic pathogens vary in the extent to which they can survive within and spread between human hosts. As shown in table 17.2, the degree of zoonotic adaptation spans a continuum from transmission only within animal populations (stage 1) to transmission only within human populations (stage 5).

Most zoonotic pathogens are not well adapted to humans (stages 2–3), emerge sporadically through spillover events, and may lead to localized outbreaks, called stuttering chains (Pike and others 2010; Wolfe and others 2005). These episodes of “viral chatter” increase pandemic risk by providing opportunities for viruses to become better adapted to spreading within a human population.

Pathogens that are past stage 3 are of the greatest concern, because they are sufficiently adapted to humans to cause long transmission chains between humans (directly or indirectly through vectors), and their geographic spread is not constrained by the habitat range of an animal reservoir.

Tips on preparing for and managing Ransomware Attacks

IBM X-Force Incident Response and Intelligence Services (IRIS) offers the following recommendations for organizations, cities, government entities, and beyond on how they can prepare for ransomware attacks:

  • Rehearse and Test Your Incident Response: It’s not a matter of if an incident response plan will be tested anymore, but a matter of when. Create a detailed incident response plan and conduct regular simulations with your stakeholders to test your response.


  • Maintain Backups, Test Backups, And Keep Offline Backups: Backing up systems is a critical best practice. Ensuring departments have effective backups of critical systems and are testing these backups is more important than ever. Store backups apart from your primary network and only allow read, not write, access to the backups. Offline backups are ideal for the most sensitive data and systems.


  • Develop an Action Plan for Quickly Establishing Temporary Functionality: Consider developing a capability to set up a short-term, quick turnaround business function to enable continued operations while an attack is being remediated. Create an alternative location and network for functions to continue critical services and systems in the face of attacks, even as remediation of or replacement of the original network is ongoing.


  • Patch Systems: Ensure all systems are patched with the latest software updates.


  • Empower Employees: Some of the best responses to cyberattacks can stem from empowered employees that are allowed to take calculated risks to save digital assets.


  • Hire an Ethical Hacker: Departments should constantly test their security measures, including testing employees to identify weaknesses. Learn your group’s risk level by having a hacker hack your department before a criminal does.