Childhood immunisation services have been disrupted by the COVID-19 pandemic in at least 68 countries during 2020 with around 80 million under 1-year-old children being affected (Nelson, 2020; Science (AAAS), 2020; UNICEF, 2020; WHO, 2020a). This has occurred for several reasons – the diversion of health care staff, facilities, and finances to deal with COVID-19 treatment and response; reluctance of individuals to bring children to be vaccinated due to fear of infection; barriers to travel due to local physical distancing measures; disruptions in vaccine supply chains; lack of personal protective equipment; and decisions to stop or postpone vaccination campaigns to reduce the risk of transmission during such campaigns.

The World Health Organization (WHO) issued guidance in March 2020 on immunisation activities during the COVID-19 pandemic (WHO, 2020b). The guidance recommended a temporary suspension of mass vaccination campaigns, but continuation of routine immunisation services by the health systems while maintaining physical distancing and infection prevention and control measures for COVID-19. Routine immunisation was one of the most disrupted services relative to other essential health services based on a WHO pulse survey in May and June 2020 that was focused on continuity of essential health services during the COVID-19 pandemic (WHO, 2020c). WHO, UNICEF, Gavi, the Vaccine Alliance, and their partners also conducted two pulse polls in April and June 2020 to understand COVID-19-related disruptions to immunisation services (WHO, 2020d). Based on respondents from 82 countries, pulse polls indicated that there was widespread disruption to routine immunisation services in addition to the suspension of mass vaccination campaigns. The main reasons reported for this disruption were low availability of personal protective equipment for healthcare workers, low availability of health workers, and travel restrictions.

Disruptions to routine health care due to the COVID-19 pandemic are projected to increase child and maternal deaths in low-income and middle-income countries (Roberton et al., 2020). No country has made a policy decision to stop routine immunisation during a COVID-19 epidemic. Risk-benefit analysis of countries in Africa shows routine immunisation to have far greater benefits than risks even in the context of the COVID-19 pandemic (LSHTM CMMID COVID-19 Working Group et al., 2020). Nevertheless, routine immunisation coverage has dropped in most countries (WHO, 2020d).

Evidence on the health impact of suspending vaccination campaigns during the COVID-19 pandemic is limited. Modelling indicates that both fixed post and door-to-door campaigns targeting under 5-year-old children may cause temporary minor increases in total SARS-CoV-2 infections (Hagedorn et al., 2020). However, avoiding campaigns during the local peak of SARS-CoV-2 transmission is key to reducing the effect size, and SARS-CoV-2 transmission during campaigns can be minimised with good personal protective equipment and limiting movement of vaccinators (Hagedorn et al., 2020). The WHO recommends that countries consider the risk of outbreaks using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the COVID-19 pandemic, but the guidance was not based on any quantitative assessment of transmission risk for either COVID-19 or existing vaccine-preventable diseases (WHO, 2020e).

Hence, countries need to assess the health impact of postponing vaccination campaigns, which can inform the epidemiological risk assessment for outbreaks due to campaign delays and prioritise which vaccines to use in campaigns (WHO, 2020f). The need for such assessments is greatest in low- and lower middle-income countries which generally have greater risks of vaccine-preventable disease outbreaks and limited health care resources to deal with COVID-19 epidemics. It is difficult to quantify the impact of different scenarios using only observational data, which does not give the counterfactual to what actually happened in 2020. To address this, we used transmission dynamic models to project alternative scenarios about postponing vaccination campaigns alongside disruption of routine immunisation, for three pathogens with high outbreak potential and for which mass vaccination campaigns are a key delivery mode alongside routine immunisation – measles, meningococcal A, and yellow fever.

Deaths and disability-adjusted life years (DALYs) due to measles, meningococcal A, and yellow fever under different routine and campaign vaccination scenarios were projected in a subset of 10 low- and lower middle-income countries over the years 2020–2030. Projections were made using previously validated transmission dynamic models; we used three models for measles, two models for meningococcal A, and two models for yellow fever (summary model details are available in Table 1a-c with full model details in Appendix Section 3; a description of the key drivers of similarities and differences between models is given in Appendix Section 4). Guidance used by the different models for DALY calculations are publicly accessible (Vaccine Impact Modelling Consortium, 2019) and a glossary of terms can be found in Appendix 1—table 15.

The chosen countries were low- and lower-middle-income countries that had planned vaccination campaigns in 2020 and were selected following consultations with partners in WHO, UNICEF, CDC and other organisations. Thereby, the selected countries differ between infections – Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan for measles; Burkina Faso, Chad, Niger, and Nigeria for meningococcal A; Democratic Republic of the Congo, Ghana, and Nigeria for yellow fever.

Models used routine and campaign vaccination coverage from WUENIC (WHO and UNICEF Estimates of National Immunization Coverage) and post campaign surveys for 2000–2019 (Li et al., 2021), and future projections of routine coverage based on assumptions agreed with disease and immunisation programme experts at the global, regional, and national levels (see Appendix 1—table 16). Assumptions for our counterfactual ‘business as usual’ scenario were determined through consultation with disease and immunisation programme experts across partners at the global, regional, and national levels. All assumptions varied by pathogen. For routine immunisation, assumptions about future coverage levels were based on historical coverage from WUENIC for 2015–19. For vaccination campaigns or supplementary immunisation activities (SIA), assumptions about future campaigns were based either on patterns of past campaigns or campaigns recommended by WHO. We explored four scenarios that assumed different levels of disruption in the year 2020 to routine immunisation and postponement of campaigns projected in the scenarios, due to COVID-19 (see Table 2). The disruption scenarios are based on 50% reduction in routine immunisation and/or suspension of campaign vaccination in 2020 and postponement to 2021. These disruption scenarios aimed to approximate plausible drops in routine coverage levels and plausible delays to campaigns due to the COVID-19 pandemic.

We estimated the health impact of these disruption scenarios in comparison to the counterfactual scenario of no disruption (BAU – business-as-usual scenario) for measles, meningococcal A, and yellow fever during 2020–2030. We estimated the health impact of routine and campaign immunisation disruption through projections of total deaths (and DALYs) per 100,000 population, excess deaths (and DALYs) per 100,000 population, and excess deaths (and DALYs) during 2020–2030 which were scaled relative to the maximum number of excess deaths (or DALYs) across all scenarios. We did not assume any changes to case-fatality risks as a result of the COVID-19 pandemic.

The models generally produce a range of stochastic realisations based on distributions of input parameters and/or posterior distributions of fitted parameters. In the results, we present output from an average scenario, which is defined differently across models based on their characteristics: model projection from mean (measles/DynaMICE) or median (YF/Imperial) of input parameters, median projection from posterior of fitted force of infection (YF/Notre Dame), mean of stochastic output projections (measles/IDM, measles/PSU, MenA/Cambridge, MenA/KP).

The health impact varies across the disruption scenarios for the three infections in the different countries. Figure 1 shows the model-predicted total deaths per 100,000 population per year during 2020–2030 (see Appendix 1—figure 1 for similar projections for DALYs impact, Table 3 and S1 for scenario averages over the entire time period, and Appendix 1—tables 3, 5, 7, and Appendix 1—table 11 for absolute numbers of deaths).

Figure 1

Download asset Open asset

In the case of measles, Bangladesh initially postponed its campaign by a few months. The two measles models give slightly different predictions about the consequences of this. The Penn State model predicts that delaying the 2020 campaign increases deaths slightly (by 0.03 per 100,000 over 2020–2030) but this increase is not seen in DynaMICE. Conversely, DynaMICE predicts an increase in deaths of 0.35 per 100,000 over 2020–2030 if routine coverage drops by 50%, but this is not seen in the Penn State model; see Appendix 1—table 2 for further details. For Ethiopia, a reduction in routine coverage is predicted to lead to outbreaks sooner and increases in overall deaths in all three models (DynaMICE, Penn State models and IDM), while postponing the 2020 campaign only increases deaths in the DynaMICE model. The Ethiopian campaign was eventually reinstated only 3 months later than scheduled. For Kenya, the disruption to routine and campaign immunisation was not predicted to lead to increased risk of outbreaks, due to high coverage of the first dose of measles vaccine and better optimally-timed campaigns in preventing outbreaks during 2020–2030, although coverage of the second-dose of measles vaccine is suboptimal. For Nigeria, either postponement of the 2020 immunisation campaign or a reduction in routine coverage is predicted to lead to increases in measles mortality in Penn State and IDM models, but not in DynaMICE. Note that these increases were predicted to be highly localised in the subnational IDM model; see Discussion for details. For South Sudan, the postponement of immunisation campaigns from 2020 to 2021 is predicted to be beneficial in averting a potential outbreak in 2022 in both DynaMICE and Penn State models (although note caveats in the Discussion about such predictions), but decreases in routine coverage are predicted to lead to more deaths, with a larger predicted increase in DynaMICE. For Chad, both DynaMICE and Penn State models predict an overall increase in deaths with routine coverage drops, but only the Penn State model predicts an increase with a postponement of campaigns. Model-specific estimates of measles deaths per 100,000 over 2020–2030 per country are provided in Appendix 1—table 2 with absolute numbers for all countries per model given in Appendix 1—table 11. Model-specific estimates of measles deaths per 100,000 per year for all countries are provided in Appendix 1—table 8.

In the case of meningococcal A (MenA), the short-term disruption to routine immunisation in Burkina Faso, Niger, Nigeria, and Chad, as well as the short-term disruption of immunisation campaigns in Nigeria and Chad would not have a significant impact on the disease incidence (see Appendix 1—table 4 for model-specific estimates by country). These four countries conducted mass preventive campaigns targeting 1- to 29-year-old populations between 2010 and 2014, and introduced the vaccine into their routine immunization schedules between 2016 and 2019. Niger and Burkina Faso completed catch-up campaigns concomitantly with the introduction into routine, and Chad and Nigeria have started but not completed their catch-up campaigns. A maximum of a 4% increase in MenA deaths over the long term is projected in either of the models and with minimal change in the short term of within 5 years. This is because of the persistence of protection against MenA due to the vaccination strategy combining mass vaccination campaign and routine introduction, which led to a lasting interruption of transmission, in particular from the direct and indirect effects of the initial mass campaigns of the 1- to 29-year-old population in 2010–2014. Model-specific estimates of meningococcal A deaths per 100,000 per year for all countries are provided in Appendix 1—table 9.

In the case of yellow fever, for the Democratic Republic of Congo and Nigeria, the postponement of immunisation campaigns from 2020 to 2021 was predicted to cause a short-term increase in burden but when campaigns were implemented, the overall burden was reduced for the time period. A reduction in routine immunization during 2020 was predicted to increase burden over the same period 2020–2030. For Ghana, the postponement of immunisation campaigns from 2020 to 2021 did not lead to an increase in yellow fever burden in the short-term, whereas a reduction in routine immunization in 2020 was predicted to increase the yellow fever burden by 0.33 or 0.07 deaths per 100,000 between 2020 and 2030 in the Imperial and Notre Dame models respectively. Model-specific estimates of excess deaths by country from 2020 to 2030 are shown in Appendix 1—table 6. Neither model was designed to specifically capture yellow fever outbreak dynamics. Therefore, although the delay of immunisation campaigns was predicted to reduce the burden of yellow fever for 2020–2030 in select settings by a small (less than 1%) amount, the increased risk of an outbreak is not accounted for in the models and this could outweigh the predicted long-term benefits. Model-specific estimates of yellow fever deaths per 100,000 per year for all countries are provided in Appendix 1—table 10.

Figure 2 shows the model-predicted excess deaths per 100,000 population per year by model for routine and campaign immunisation disruption scenarios in comparison to no disruption scenario for measles, meningococcal A, and yellow fever. The excess deaths are summed over 2020–2030 (see Appendix 1—figure 2 for similar projections for DALYs impact). The scale of excess mortality due to the immunisation service disruptions are higher for measles vaccination in comparison to meningococcal A and yellow fever vaccination; indeed excess mortality is minimal for meningococcal A.

Figure 2

Download asset Open asset

Appendix 1—figure 3 shows the normalised model-predicted excess deaths per year and country by model for routine and campaign immunisation disruption scenarios in comparison to the no disruption scenario for measles, meningococcal A, and yellow fever, with excess deaths summed and normalised over 2020–2030 (see Appendix 1—figure 4 for similar projections for DALYs impact). For measles, there are differences between models but usually a 50% reduction in routine immunisation was projected to increase the excess deaths the most in comparison to scenarios involving the postponement of immunisation campaigns from 2020 to 2021. For MenA, a 50% reduction in routine immunisation and the postponement of immunisation campaigns from 2020 to 2021 was projected to increase the excess deaths the most for Chad, although the scale of absolute impact is minimal (see Figure 2). For yellow fever, a reduction in routine immunisation was projected to increase the excess deaths the most (either in conjunction with campaign delay or not). Whilst the postponement of immunisation campaigns from 2020 to 2021 appears to have a beneficial impact of lower deaths in comparison to immunisation campaigns in 2020 for the Democratic Republic of the Congo in Appendix 1—figure 3, this does not capture the short-term increase in burden due to the missed campaign. The beneficial effect is due solely to the proportionally larger campaign implemented in 2021, that is a campaign with the same coverage leads to more fully vaccinated persons as the population grows.

The health impact of routine immunisation service disruptions and mass vaccination campaign suspensions due to the COVID-19 pandemic differs widely between infections and countries, so decision-makers need to consider their local epidemiological situation. For meningococcal A and yellow fever, we predict that postponing campaigns has a minimal short-term effect because both pathogens have a low effective reproduction number and strong existing herd immunity from recent campaigns in the countries modelled (see Appendix 1—table 17 for list of campaigns). However, this is influenced by the model structures and their propensity to capture outbreak dynamics, which particularly affects the predictions for yellow fever. For measles, in some countries such as Ethiopia and Nigeria, even a 1-year postponement of immunisation campaigns could have led to large outbreaks, but both countries were able to implement planned SIAs in 2020 after a few months’ delay. In other countries with high routine immunisation coverage and/or recent campaigns, SIAs may be postponed by a year without causing large outbreaks. However, model projections about future outbreaks differ between models in terms of both timing and magnitude. These differences capture uncertainty around data and model structure that differ between models.

In some of our modelled scenarios, postponement of immunisation campaigns does not appear to increase overall cases, if the delay time-period is less than the interval to the next outbreak. Such a scenario is inferred in the immunisation disruption scenarios for postponement of measles campaigns for South Sudan. This does not imply that a postponement is preferred, as we do not take into account other contextual or programmatic factors; rather it reflects the effectiveness of campaigns in closing the immunity gaps and the demographic effect of including more children in delayed campaigns. In instances with very low routine immunisation coverage, there is a possibility that the vaccination campaign is the main opportunity for missed children to be vaccinated. Thus, for the same proportion of the same age group targeted by campaigns, more children will be vaccinated for the same coverage levels in countries with birth rates increasing over time. While these results may be useful in the COVID-19 context, there is also considerable uncertainty around both model findings and data inputs such as incidence and vaccine coverage that prohibits further general comment on the optimal timing of campaigns.

The measles immunisation campaigns for 2020 in Nigeria were specifically targeted at Kogi and Niger states, states that were originally scheduled for inclusion in the campaigns for 2019 across northern Nigeria which were delayed for other reasons. Given the localised build-up of susceptibility in these two states due to low routine immunisation coverage and the long window between campaigns, IDM’s subnational Nigeria model indicated that further campaign delays would result in a high risk of localised outbreaks in these states (one potential explanation for the IDM model predicting worse consequences of delays in these campaigns than the other models). Campaigns targeted specifically to these two states were implemented in October 2020. In general, for countries where routine immunisation coverage was low even before the COVID-19 pandemic, the build-up of the susceptible population from low routine immunisation coverage over 2–3 years between campaigns enhances the risk of outbreaks more than recent and temporary disruptions to routine immunisation. Further, our models did not include the possibility that COVID-19 restrictions may have temporarily reduced measles transmissibility and the risk of measles outbreaks due to reduced chance of introduction of infection into populations with immunity gaps. This risk rises again rapidly once travel restrictions and physical distancing are relaxed. This is an additional reason (which we do not model) for implementing postponed immunisation campaigns at the earliest opportunity to prevent measles outbreaks as COVID-19 restrictions are lifted (LSHTM CMMID COVID-19 Working Group et al., 2021).

While the degree of health impact of service disruptions varies, the models generally show that reductions in routine immunisation coverage have a far greater impact on predicted excess deaths over the next decade for all infections modeled than postponement of campaigns. This has significant implications for countries planning catch-up strategies and highlights the need for increased emphasis on the importance of implementing catch-up as an ongoing part of routine immunisation (WHO, 2020f).

The disease burden averted by measles and meningococcal A vaccination are primarily among under-5-year-old and under-10-year-old children respectively, and disease burden averted by yellow fever vaccination are among younger age-group individuals. Since children and younger age-group individuals are at relatively lower risk of morbidity and mortality from COVID-19 in comparison to elderly populations, the health benefits of sustaining measles, meningococcal A, and yellow fever immunisation programmes during the COVID-19 pandemic outweigh the excess SARS-CoV-2 infection risk to these age groups that are associated with vaccination service delivery points. Thereby, the delivery of measles, meningococcal A, and yellow fever immunisation services should continue, as logistically as possible, by adapting service delivery in a COVID-secure manner with implementation of SARS-CoV-2 infection prevention and control measures.

Our study has limitations and we have not considered logistical constraints posed by the COVID-19 prevention and control measures on vaccine supply, demand for vaccination, access, and health workforce. Future introduction of COVID-19 vaccination may also divert the workforce normally conducting campaigns for other vaccines. Our models do not reflect geographical heterogeneity sub-nationally, whereas in reality this is a key feature. Nor do we incorporate known seasonality of infections, which may affect the window of opportunity for catching up. The models used in this analysis, in particular for yellow fever, are best suited to capture long-term changes in disease burden due to vaccination and cannot capture outbreak dynamics that may arise in the short-term. A key strength of our analysis is that we used two to three models for each infection, which allowed investigation of whether projections were sensitive to model structure and assumptions. Each model had different strengths and limitations. For instance, some models measured epidemic properties like reproduction numbers directly, while other models used estimates from other studies. We did indeed find quantitative differences between models of the same infection, but most models agreed on the countries in which disruptions had the largest effect on disease burden.

A further limitation is the omission of changes to transmission in the three pathogens due to COVID-19 mitigation measures. This is a critical area that needs further investigation; however, all three included pathogens have substantially different dynamics to those of SARS-CoV-2. For yellow fever, the majority of transmission is sylvatic rather than person-to-person, so COVID-19 mitigation measures are unlikely to have a major effect on incidence, unless they decrease contact between humans and the sylvatic cycle. For meningococcal A, we find that even with a decrease in vaccine coverage there is limited potential for outbreaks, so decreased transmission due to COVID-19 non-pharmaceutical interventions will only reinforce this. For measles, there is the potential for non-pharmaceutical interventions to decrease transmission. However, measles is much more transmissible than COVID-19 (with R₀ usually well above 10 rather than 2–5 Guerra et al., 2017), and transmission is generally concentrated among very young children rather than adults. Hence it is unclear whether interventions designed for COVID-19 (mask wearing, closure of schools, workplaces and retail, travel restrictions etc.) will be able to prevent measles outbreaks. Further, while COVID-19 mitigation measures may temporarily reduce measles transmissibility and outbreak risk from measles immunity gaps, the risk for measles outbreaks will rise rapidly once COVID-19-related contact restrictions are lifted (LSHTM CMMID COVID-19 Working Group et al., 2021), which happens at different rates in different parts of countries.

We conducted our health impact assessment to align with the WHO framework for decision making using an evidence-based approach to assist in prioritisation of vaccines and strategies for catch-up vaccination during the COVID-19 pandemic (WHO, 2020f). The framework highlights three main steps, with the primary step being an epidemiological risk assessment for each disease based on the burden of disease and population immunity, as well as the risk factors associated with the immunisation service disruptions. The second step focuses on the amenability of delivery strategies and operational factors for each vaccine, and the third step on the assessment of contextual factors and competing needs.

Our health impact assessment addresses in part the primary step of an epidemiological risk assessment by estimating the disease burden for different immunisation scenarios, but does not include the health impact assessment of excess COVID-19 disease burden attributable to these immunisation scenarios. While we have assessed the immunity gaps caused by immunisation service disruptions for measles, meningococcal A, and yellow fever vaccination in 10 low- and lower middle-income countries, sustaining routine immunisation and resuming immunisation campaigns during the COVID-19 pandemic requires adaptations to service delivery with additional safety measures to protect the health workers and the community from SARS-CoV-2 infection (Banks and Boonstoppel, 2020). Infection prevention and control measures include personal protective equipment for health workers, children to be vaccinated, and their parents or caregivers; additional prevention and control measures against SARS-CoV-2 infection at vaccination sites; physical distancing; and symptomatic screening and triaging (WHO, 2020e). COVID-19 transmission may be further mitigated by delivering several vaccines during a single campaign (such as measles and polio vaccines), or even combining vaccines with other age-relevant interventions such as nutritional supplements. Further, social mobilisation is needed to address the rumours, misinformation, and fear among the community to access vaccination safely during the COVID-19 pandemic (WHO, 2020g). Therefore, our health impact assessment needs to be followed up by planning and implementation of catch-up vaccination to close the immunity gaps using a mixture of locally appropriate strategies to strengthen immunisation (Cutts et al., 2021), alongside access to additional operating costs to conduct routine and campaign immunisation services safely in COVID-secure environments while considering contextual factors and competing needs.

All code, data inputs and outputs used to generate the results in the manuscript (apart from projections about vaccine coverage beyond 2020 which are commercially confidential property of Gavi) are available at: https://github.com/vimc/vpd-covid-phase-I (copy archived at https://archive.softwareheritage.org/swh:1:rev:ebff9a24b8b7c9a7c6c5c77f783f2435a57d1d2b).

1. Software

   1. Banks C
   2. Boonstoppel L

   (2020) Immunization Campaigns During the COVID-19 Pandemic: A Rapid Analysis of The Additional Operational Cost

   Immunization Campaigns During the COVID-19 Pandemic: A Rapid Analysis of The Additional Operational Cost.

   https://thinkwell.global/cost-of-conducting-immunization-campaigns-during-covid-19/
2. 1. Campagne G
   2. Schuchat A
   3. Djibo S
   4. Ousséini A
   5. Cissé L
   6. Chippaux JP

   (1999)

   Epidemiology of bacterial meningitis in Niamey, Niger, 1981-96

   Bulletin of the World Health Organization 77:499–508.

   • PubMed
   • Google Scholar
3. 1. Chen S
   2. Fricks J
   3. Ferrari MJ

   (2012) Tracking measles infection through non-linear state space models

   Journal of the Royal Statistical Society: Series C 61:117–134.

   https://doi.org/10.1111/j.1467-9876.2011.01001.x

   • Google Scholar
4. 1. Cutts FT
   2. Ferrari MJ
   3. Krause LK
   4. Tatem AJ
   5. Mosser JF

   (2021) Vaccination strategies for measles control and elimination: time to strengthen local initiatives

   BMC Medicine 19:2.

   https://doi.org/10.1186/s12916-020-01843-z

   • PubMed
   • Google Scholar
5. 1. Edmond K
   2. Clark A
   3. Korczak VS
   4. Sanderson C
   5. Griffiths UK
   6. Rudan I

   (2010) Global and regional risk of disabling sequelae from bacterial meningitis: a systematic review and meta-analysis

   The Lancet Infectious Diseases 10:317–328.

   https://doi.org/10.1016/S1473-3099(10)70048-7

   • PubMed
   • Google Scholar
6. 1. Eilertson KE
   2. Fricks J
   3. Ferrari MJ

   (2019) Estimation and prediction for a mechanistic model of measles transmission using particle filtering and maximum likelihood estimation

   Statistics in Medicine 38:4146–4158.

   https://doi.org/10.1002/sim.8290

   • PubMed
   • Google Scholar
7. 1. Gaythorpe KAM
   2. Jean K
   3. Cibrelus L
   4. Garske T

   (2019) Quantifying model evidence for yellow fever transmission routes in Africa

   PLOS Computational Biology 15:e1007355.

   https://doi.org/10.1371/journal.pcbi.1007355

   • PubMed
   • Google Scholar
8. Software

   1. Gaythorpe KAM

   (2021a) vpd-covid-phase-I, version swh:1:rev:ebff9a24b8b7c9a7c6c5c77f783f2435a57d1d2b

   Software Heritage.

   https://archive.softwareheritage.org/swh:1:dir:34be4f2bb933a5d6e16cc9d0017ce35138684836;origin=https://github.com/vimc/vpd-covid-phase-I;visit=swh:1:snp:a658816662a429243e449e86090c7f408424ec4a;anchor=swh:1:rev:ebff9a24b8b7c9a7c6c5c77f783f2435a57d1d2b
9. 1. Gaythorpe KA
   2. Hamlet A
   3. Jean K
   4. Garkauskas Ramos D
   5. Cibrelus L
   6. Garske T
   7. Ferguson N

   (2021b) The global burden of yellow fever

   eLife 10:e64670.

   https://doi.org/10.7554/eLife.64670

   • PubMed
   • Google Scholar
10. 1. Guerra FM
    2. Bolotin S
    3. Lim G
    4. Heffernan J
    5. Deeks SL
    6. Li Y
    7. Crowcroft NS

    (2017) The basic reproduction number (R₀) of measles: a systematic review

    The Lancet Infectious Diseases 17:e420–e428.

    https://doi.org/10.1016/S1473-3099(17)30307-9

    • PubMed
    • Google Scholar
11. Report

    1. Hagedorn B
    2. Frey K
    3. IDM COVID-19 Response Team

    (2020)

    The Effect of Regional Under-Age-5 Campaigns on the SARS-CoV-2 Outbreak in LMIC

    The Institute for Disease Modeling.

    • Google Scholar
12. 1. Hughes SL
    2. Bolotin S
    3. Khan S
    4. Li Y
    5. Johnson C
    6. Friedman L
    7. Tricco AC
    8. Hahné SJM
    9. Heffernan JM
    10. Dabbagh A
    11. Durrheim DN
    12. Orenstein WA
    13. Moss WJ
    14. Jit M
    15. Crowcroft NS

    (2020) The effect of time since measles vaccination and age at first dose on measles vaccine effectiveness - A systematic review

    Vaccine 38:460–469.

    https://doi.org/10.1016/j.vaccine.2019.10.090

    • PubMed
    • Google Scholar
13. Software

    1. IDM

    (2020) Epidemiological MODeling (EMOD) Software for General Disease. Institute for Disease Modeling

    Epidemiological MODeling (EMOD) Software for General Disease. Institute for Disease Modeling.

    https://docs.idmod.org/projects/emod-generic/en/latest/index.html
14. 1. Institute for Disease Modeling
    2. Bershteyn A
    3. Gerardin J
    4. Bridenbecker D
    5. Lorton CW
    6. Bloedow J
    7. Baker RS
    8. Chabot-Couture G
    9. Chen Y
    10. Fischle T
    11. Frey K
    12. Gauld JS
    13. Hu H
    14. Izzo AS
    15. Klein DJ
    16. Lukacevic D
    17. McCarthy KA
    18. Miller JC
    19. Ouedraogo AL
    20. Perkins TA
    21. Steinkraus J
    22. Ten Bosch QA
    23. Ting HF
    24. Titova S
    25. Wagner BG
    26. Welkhoff PA
    27. Wenger EA
    28. Wiswell CN

    (2018) Implementation and applications of EMOD, an individual-based multi-disease modeling platform

    Pathogens and Disease 76:fty059.

    https://doi.org/10.1093/femspd/fty059

    • PubMed
    • Google Scholar
15. 1. Irving TJ
    2. Blyuss KB
    3. Colijn C
    4. Trotter CL

    (2012) Modelling meningococcal meningitis in the African meningitis belt

    Epidemiology and Infection 140:897–905.

    https://doi.org/10.1017/S0950268811001385

    • PubMed
    • Google Scholar
16. 1. Jackson ML
    2. Diallo AO
    3. Médah I
    4. Bicaba BW
    5. Yaméogo I
    6. Koussoubé D
    7. Ouédraogo R
    8. Sangaré L
    9. Mbaeyi SA

    (2018) Initial validation of a simulation model for estimating the impact of serogroup A Neisseria meningitidis vaccination in the African meningitis belt

    PLOS ONE 13:e0206117.

    https://doi.org/10.1371/journal.pone.0206117

    • PubMed
    • Google Scholar
17. 1. Jean K
    2. Donnelly CA
    3. Ferguson NM
    4. Garske T

    (2016) A Meta-Analysis of serological response associated with yellow fever vaccination

    The American Journal of Tropical Medicine and Hygiene 95:1435–1439.

    https://doi.org/10.4269/ajtmh.16-0401

    • PubMed
    • Google Scholar
18. 1. Johansson MA
    2. Vasconcelos PF
    3. Staples JE

    (2014) The whole iceberg: estimating the incidence of yellow fever virus infection from the number of severe cases

    Transactions of the Royal Society of Tropical Medicine and Hygiene 108:482–487.

    https://doi.org/10.1093/trstmh/tru092

    • PubMed
    • Google Scholar
19. 1. Karachaliou A
    2. Conlan AJ
    3. Preziosi MP
    4. Trotter CL

    (2015) Modeling Long-term vaccination strategies with MenAfriVac in the African meningitis belt

    Clinical Infectious Diseases 61 Suppl 5:S594–S600.

    https://doi.org/10.1093/cid/civ508

    • PubMed
    • Google Scholar
20. 1. Karachaliou Prasinou A
    2. Conlan AJK
    3. Trotter CL

    (2021) Understanding the role of duration of vaccine protection with MenAfriVac: simulating alternative vaccination strategies

    Microorganisms 9:461.

    https://doi.org/10.3390/microorganisms9020461

    • PubMed
    • Google Scholar
21. 1. Li X
    2. Mukandavire C
    3. Cucunubá ZM
    4. Echeverria Londono S
    5. Abbas K
    6. Clapham HE
    7. Jit M
    8. Johnson HL
    9. Papadopoulos T
    10. Vynnycky E
    11. Brisson M
    12. Carter ED
    13. Clark A
    14. de Villiers MJ
    15. Eilertson K
    16. Ferrari MJ
    17. Gamkrelidze I
    18. Gaythorpe KAM
    19. Grassly NC
    20. Hallett TB
    21. Hinsley W
    22. Jackson ML
    23. Jean K
    24. Klepac P
    25. Lessler J
    26. Li X
    27. Moore SM
    28. Nayagam S
    29. Nguyen DM
    30. Razavi H
    31. Razavi-Shearer D
    32. Resch S
    33. Sanderson C
    34. Sweet S
    35. Sy S
    36. Tam Y
    37. Tanvir H
    38. Tran QM
    39. Trotter CL
    40. Truelove S
    41. van Zandvoort K
    42. Verguet S
    43. Walker N
    44. Winter A
    45. Woodruff K
    46. Ferguson NM
    47. Garske T
    48. Vaccine Impact Modelling Consortium

    (2021) Estimating the health impact of vaccination against ten pathogens in 98 low-income and middle-income countries from 2000 to 2030: a modelling study

    The Lancet 397:398–408.

    https://doi.org/10.1016/S0140-6736(20)32657-X

    • PubMed
    • Google Scholar
22. 1. Lingani C
    2. Bergeron-Caron C
    3. Stuart JM
    4. Fernandez K
    5. Djingarey MH
    6. Ronveaux O
    7. Schnitzler JC
    8. Perea WA

    (2015) Meningococcal meningitis surveillance in the African meningitis belt, 2004-2013

    Clinical Infectious Diseases 61 Suppl 5:S410–S415.

    https://doi.org/10.1093/cid/civ597

    • PubMed
    • Google Scholar
23. 1. LSHTM CMMID COVID-19 Working Group
    2. Abbas K
    3. Procter SR
    4. van Zandvoort K
    5. Clark A
    6. Funk S
    7. Mengistu T
    8. Hogan D
    9. Dansereau E
    10. Jit M
    11. Flasche S

    (2020) Routine childhood immunisation during the COVID-19 pandemic in Africa: a benefit-risk analysis of health benefits versus excess risk of SARS-CoV-2 infection

    The Lancet Global Health 8:e1264–e1272.

    https://doi.org/10.1016/S2214-109X(20)30308-9

    • PubMed
    • Google Scholar
24. 1. LSHTM CMMID COVID-19 Working Group
    2. Mburu CN
    3. Ojal J
    4. Chebet R
    5. Akech D
    6. Karia B
    7. Tuju J
    8. Sigilai A
    9. Abbas K
    10. Jit M
    11. Funk S
    12. Smits G
    13. van Gageldonk PGM
    14. van der Klis FRM
    15. Tabu C
    16. Nokes DJ
    17. Scott J
    18. Flasche S
    19. Adetifa I

    (2021) The importance of supplementary immunisation activities to prevent measles outbreaks during the COVID-19 pandemic in Kenya

    BMC Medicine 19:35.

    https://doi.org/10.1186/s12916-021-01906-9

    • PubMed
    • Google Scholar
25. 1. Mossong J
    2. Hens N
    3. Jit M
    4. Beutels P
    5. Auranen K
    6. Mikolajczyk R
    7. Massari M
    8. Salmaso S
    9. Tomba GS
    10. Wallinga J
    11. Heijne J
    12. Sadkowska-Todys M
    13. Rosinska M
    14. Edmunds WJ

    (2008) Social contacts and mixing patterns relevant to the spread of infectious diseases

    PLOS Medicine 5:e74.

    https://doi.org/10.1371/journal.pmed.0050074

    • PubMed
    • Google Scholar
26. 1. Nelson R

    (2020) COVID-19 disrupts vaccine delivery

    The Lancet Infectious Diseases 20:546.

    https://doi.org/10.1016/S1473-3099(20)30304-2

    • PubMed
    • Google Scholar
27. Preprint

    1. Perkins TA
    2. Huber JH
    3. Minh QT
    4. Oidtman RJ
    5. Walters MK
    6. Siraj AS
    7. Moore SM

    (2021) Burden is in the eye of the beholder: sensitivity of yellow fever disease burden estimates to modeling assumptions

    medRxiv.

    https://doi.org/10.1101/2021.01.06.21249311

    • Google Scholar
28. 1. Portnoy A
    2. Jit M
    3. Helleringer S
    4. Verguet S

    (2018) Impact of measles supplementary immunization activities on reaching children missed by routine programs

    Vaccine 36:170–178.

    https://doi.org/10.1016/j.vaccine.2017.10.080

    • PubMed
    • Google Scholar
29. 1. Roberton T
    2. Carter ED
    3. Chou VB
    4. Stegmuller AR
    5. Jackson BD
    6. Tam Y
    7. Sawadogo-Lewis T
    8. Walker N

    (2020) Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study

    The Lancet Global Health 8:e901–e908.

    https://doi.org/10.1016/S2214-109X(20)30229-1

    • PubMed
    • Google Scholar
30. Software

    1. Science (AAAS)

    (2020) Polio, Measles, Other Diseases Set to Surge as COVID-19 Forces Suspension of Vaccination Campaigns

    Polio, Measles, Other Diseases Set to Surge as COVID-19 Forces Suspension of Vaccination Campaigns.

    https://www.sciencemag.org/news/2020/04/polio-measles-other-diseases-set-surge-covid-19-forces-suspension-vaccination-campaigns
31. 1. Simons E
    2. Ferrari M
    3. Fricks J
    4. Wannemuehler K
    5. Anand A
    6. Burton A
    7. Strebel P

    (2012) Assessment of the 2010 global measles mortality reduction goal: results from a model of surveillance data

    The Lancet 379:2173–2178.

    https://doi.org/10.1016/S0140-6736(12)60522-4

    • PubMed
    • Google Scholar
32. 1. Tartof S
    2. Cohn A
    3. Tarbangdo F
    4. Djingarey MH
    5. Messonnier N
    6. Clark TA
    7. Kambou JL
    8. Novak R
    9. Diomandé FV
    10. Medah I
    11. Jackson ML

    (2013) Identifying optimal vaccination strategies for serogroup A Neisseria meningitidis conjugate vaccine in the African meningitis belt

    PLOS ONE 8:e63605.

    https://doi.org/10.1371/journal.pone.0063605

    • PubMed
    • Google Scholar
33. 1. Thakkar N
    2. Gilani SSA
    3. Hasan Q
    4. McCarthy KA

    (2019) Decreasing measles burden by optimizing campaign timing

    PNAS 116:11069–11073.

    https://doi.org/10.1073/pnas.1818433116

    • PubMed
    • Google Scholar
34. Report

    1. UNICEF

    (2020)

    Tracking the Situation of Children During COVID-19 - UNICEF DATA

    unichef.

    • Google Scholar
35. Software

    1. Vaccine Impact Modelling Consortium

    (2019)

    General Guidance for DALYs Calculation

    General Guidance for DALYs Calculation.
36. 1. Verguet S
    2. Johri M
    3. Morris SK
    4. Gauvreau CL
    5. Jha P
    6. Jit M

    (2015) Controlling measles using supplemental immunization activities: a mathematical model to inform optimal policy

    Vaccine 33:1291–1296.

    https://doi.org/10.1016/j.vaccine.2014.11.050

    • PubMed
    • Google Scholar
37. 1. White M
    2. Idoko O
    3. Sow S
    4. Diallo A
    5. Kampmann B
    6. Borrow R
    7. Trotter C

    (2019) Antibody kinetics following vaccination with MenAfriVac: an analysis of serological data from randomised trials

    The Lancet Infectious Diseases 19:327–336.

    https://doi.org/10.1016/S1473-3099(18)30674-1

    • PubMed
    • Google Scholar
38. Software

    1. WHO

    (2019a) Feasibility Assessment of Measles and Rubella Eradication, SAGE Meeting of October 2019

    Feasibility Assessment of Measles and Rubella Eradication, SAGE Meeting of October 2019.

    https://www.who.int/immunization/sage/meetings/2019/october/Feasibility_Assessment_of_Measles_and_Rubella_Eradication_final.pdf
39. Software

    1. WHO

    (2019b) Measles–rubella eradication investment case | Weekly Epidemiological Record

    Measles–rubella eradication investment case | Weekly Epidemiological Record.

    https://www.who.int/wer/2019/wer9451_52/en/
40. Software

    1. WHO

    (2020a) At Least 80 Million Children Under One at Risk of Diseases Such as Diphtheria, Measles and Polio as COVID-19 Disrupts Routine Vaccination Efforts, Warn Gavi, WHO and UNICEF

    At Least 80 Million Children Under One at Risk of Diseases Such as Diphtheria, Measles and Polio as COVID-19 Disrupts Routine Vaccination Efforts, Warn Gavi, WHO and UNICEF.

    https://www.who.int/news-room/detail/22-05-2020-at-least-80-million-children-under-one-at-risk-of-diseases-such-as-diphtheria-measles-and-polio-as-covid-19-disrupts-routine-vaccination-efforts-warn-gavi-who-and-unicef
41. Software

    1. WHO

    (2020b) Guiding principles for immunization activities during the COVID-19 pandemic: interim guidance

    Guiding principles for immunization activities during the COVID-19 pandemic: interim guidance.

    https://apps.who.int/iris/handle/10665/331590
42. Software

    1. WHO

    (2020c) Pulse survey on continuity of essential health services during the COVID-19 pandemic: interim report

    Pulse survey on continuity of essential health services during the COVID-19 pandemic: interim report.

    https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS_continuity-survey-2020.1
43. Software

    1. WHO

    (2020d) Immunization and COVID-19 - Second pulse poll offers a more detailed understanding of disruptions to vaccination caused by COVID-19 and how to respond

    Immunization and COVID-19 - Second pulse poll offers a more detailed understanding of disruptions to vaccination caused by COVID-19 and how to respond.

    https://www.who.int/publications/m/item/gin-june-2020
44. Software

    1. WHO

    (2020e) Framework for decision-making: implementation of mass vaccination campaigns in the context of COVID-19

    Framework for decision-making: implementation of mass vaccination campaigns in the context of COVID-19.

    https://www.who.int/publications/i/item/WHO-2019-nCoV-Framework_Mass_Vaccination-2020.1
45. Software

    1. WHO

    (2020f) Leave No One Behind: Guidance for Planning and Implementing Catch-up Vaccination

    Leave No One Behind: Guidance for Planning and Implementing Catch-up Vaccination.

    https://www.who.int/teams/immunization-vaccines-and-biologicals/essential-programme-on-immunization/implementation/catch-up-vaccination/
46. Report

    1. WHO

    (2020g)

    Maintaining Essential Health Services

    Operational Guidance for the COVID-19 Context Interim Guidance.

    • Google Scholar
47. 1. Wolfson LJ
    2. Grais RF
    3. Luquero FJ
    4. Birmingham ME
    5. Strebel PM

    (2009) Estimates of measles case fatality ratios: a comprehensive review of community-based studies

    International Journal of Epidemiology 38:192–205.

    https://doi.org/10.1093/ije/dyn224

    • PubMed
    • Google Scholar
48. Software

    1. WorldPop

    (2016) Open Spatial Demographic Data and Research

    Open Spatial Demographic Data and Research.

    https://www.worldpop.org/
49. 1. Yaro S
    2. Njanpop Lafourcade BM
    3. Ouangraoua S
    4. Ouoba A
    5. Kpoda H
    6. Findlow H
    7. Tall H
    8. Seanehia J
    9. Martin C
    10. Ouedraogo JB
    11. Gessner B
    12. Meda N
    13. Borrow R
    14. Trotter C
    15. Mueller JE

    (2019) Antibody persistence at the population level 5 years after mass vaccination with meningococcal serogroup A conjugate vaccine (PsA-TT) in Burkina Faso: need for a booster campaign?

    Clinical Infectious Diseases 68:435–443.

    https://doi.org/10.1093/cid/ciy488

    • PubMed
    • Google Scholar
50. 1. Yellow Fever Expert Committee
    2. Garske T
    3. Van Kerkhove MD
    4. Yactayo S
    5. Ronveaux O
    6. Lewis RF
    7. Staples JE
    8. Perea W
    9. Ferguson NM

    (2014) Yellow fever in Africa: estimating the burden of disease and impact of mass vaccination from outbreak and serological data

    PLOS Medicine 11:e1001638.

    https://doi.org/10.1371/journal.pmed.1001638

    • PubMed
    • Google Scholar
51. 1. Zimmermann M
    2. Frey K
    3. Hagedorn B
    4. Oteri AJ
    5. Yahya A
    6. Hamisu M
    7. Mogekwu F
    8. Shuaib F
    9. McCarthy KA
    10. Chabot-Couture G

    (2019) Optimization of frequency and targeting of measles supplemental immunization activities in Nigeria: a cost-effectiveness analysis

    Vaccine 37:6039–6047.

    https://doi.org/10.1016/j.vaccine.2019.08.050

    • PubMed
    • Google Scholar

Author details

1. Katy AM Gaythorpe

   MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Kaja Abbas, John Huber, Andromachi Karachaliou and Niket Thakkar

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3734-9081

2. Kaja Abbas

   Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Katy AM Gaythorpe, John Huber, Andromachi Karachaliou and Niket Thakkar

   Competing interests

   No competing interests declared

3. John Huber

   Department of Biological Sciences, University of Notre Dame, South Bend, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Katy AM Gaythorpe, Kaja Abbas, Andromachi Karachaliou and Niket Thakkar

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5245-5187

4. Andromachi Karachaliou

   Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Katy AM Gaythorpe, Kaja Abbas, John Huber and Niket Thakkar

   Competing interests

   No competing interests declared

5. Niket Thakkar

   Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Katy AM Gaythorpe, Kaja Abbas, John Huber and Andromachi Karachaliou

   Competing interests

   NT and KM are employees of the Institute for Disease Modeling at the Bill and Melinda Gates Foundation. This publication is based on research funded in part by the Bill and Melinda Gates Foundation, including but not limited to models and data analysis performed by the Institute for Disease Modeling at the Bill and Melinda Gates Foundation.

6. Kim Woodruff

   MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4618-8267

7. Xiang Li

   MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

8. Susy Echeverria-Londono

   MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

9. VIMC Working Group on COVID-19 Impact on Vaccine Preventable Disease

   Contribution

   Conceptualization, Data curation, Visualization, Writing - review and editing

   Competing interests

   FC declares consultancy fees from the Bill and Melinda Gates Foundation. TM is an employee of Gavi, which funded the research. ED and LKK are employees of the Bill and Melinda Gates Foundation, which funded the research.

   1. Andre Arsene Bita Fouda, World Health Organization - Regional Office for Africa, Brazzaville, Democratic Republic of the Congo
   2. Felicity Cutts, London School of Hygiene & Tropical Medicine, London, United Kingdom
   3. Emily Dansereau, Bill & Melinda Gates Foundation, Seattle, United States
   4. Antoine Durupt, World Health Organization, Geneva, Switzerland
   5. Ulla Griffiths, United Nations Children’s Fund (UNICEF), New York, United States
   6. Jennifer Horton, World Health Organization, Geneva, Switzerland
   7. L Kendall Krause, Bill & Melinda Gates Foundation, Seattle, United States
   8. Katrina Kretsinger, World Health Organization, Geneva, Switzerland
   9. Tewodaj Mengistu, Gavi, the Vaccine Alliance, Geneva, Switzerland
   10. Imran Mirza, United Nations Children’s Fund (UNICEF), New York, United States
   11. Simon R Procter, London School of Hygiene & Tropical Medicine, London, United Kingdom
   12. Stephanie Shendale, World Health Organization, Geneva, Switzerland

10. Matthew Ferrari

    Pennsylvania State University, University Park, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Michael L Jackson, Kevin McCarthy, T Alex Perkins, Caroline Trotter and Mark Jit

    Competing interests

    No competing interests declared

11. Michael L Jackson

    Kaiser Permanante Washington, Seattle, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Matthew Ferrari, Kevin McCarthy, T Alex Perkins, Caroline Trotter and Mark Jit

    Competing interests

    No competing interests declared

12. Kevin McCarthy

    Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Matthew Ferrari, Michael L Jackson, T Alex Perkins, Caroline Trotter and Mark Jit

    Competing interests

    NT and KM are employees of the Institute for Disease Modeling at the Bill and Melinda Gates Foundation. This publication is based on research funded in part by the Bill and Melinda Gates Foundation, including but not limited to models and data analysis performed by the Institute for Disease Modeling at the Bill and Melinda Gates Foundation.

13. T Alex Perkins

    Department of Biological Sciences, University of Notre Dame, South Bend, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Matthew Ferrari, Michael L Jackson, Kevin McCarthy, Caroline Trotter and Mark Jit

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7518-4014

14. Caroline Trotter

    Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom

    Contribution

    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Matthew Ferrari, Michael L Jackson, Kevin McCarthy, T Alex Perkins and Mark Jit

    Competing interests

    CT declares a consultancy fee from GSK in 2018 (unrelated to the submitted work).

15. Mark Jit

    1. Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
    2. School of Public Health, University of Hong Kong, Hong Kong SAR, China

    Contribution

    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Matthew Ferrari, Michael L Jackson, Kevin McCarthy, T Alex Perkins and Caroline Trotter

    For correspondence

    Mark.Jit@lshtm.ac.uk

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0001-6658-8255

• 2,874

  views

• 384

  downloads

• 56

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.