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Background: Primary healthcare systems require adequate staffing to meet the needs of their local population. Guidelines typically use population ratio targets for healthcare workers, such as Ethiopia’s goal of two health extension workers for every five thousand people. However, fixed ratios do not reflect local demographics, fertility rates, disease burden (e.g., malaria endemicity), or trends in these values. Recognizing this, we set out to estimate the clinical workload to meet the primary healthcare needs in Ethiopia by region. Methods: We utilize the open-source modeling package PACE-HRH for our analysis. This is a stochastic Monte Carlo simulation model, which samples annually from distributions for fertility, mortality, disease burden, and the trends in these rates. Inputs were drawn from literature, DHS, and UN population estimates. We model seven regions and two charted cities of Ethiopia, based on data availability and the anticipated reliability of historical trends into the future. Results: All regions and charted cities are expected to experience increased workload between 2021 and 2035 for a starting catchment of five thousand people. The expected (mean) clinical workload varied from 2,930 hours (Addis) to 3,752 (Gambela) and increased by 19-28% over fifteen years. This results from a decline in per capita workload (due to declines in fertility and infectious diseases), overpowered by total population growth. Pregnancy, non-communicable diseases, sick child care, and nutrition remain the largest service categories, but their priority shifts substantially in some regions by 2035. Sensitivity analysis shows that fertility assumptions have major implications for workload. We incorporate seasonality and estimate monthly variation of up to 8.9% (Somali), though most services with high variability are declining. Conclusions: Regional variation in demographics, fertility, seasonality, and disease trends all affect the workload estimates. This results in differences in expected clinical workload, the level of uncertainty in those estimates, and relative priorities between service categories. By showing these differences, we demonstrate the inadequacy of a fixed population ratio for staffing allocation. Policy-makers and regulators need to consider these factors in designing their healthcare systems, or they risk sub-optimally allocating workforce and creating inequitable access to care.

Background Effective healthcare systems need adequate numbers of well-trained human resources for health (HRH). To support evidence-based strategic planning, modeling is sometimes used to estimate the number of required health workers and to allocate them appropriately. However, despite the demonstrated utility of models, there are several limitations to existing tools, including the inability to reflect the stochastic nature of workload and parameter uncertainty, or to incorporate seasonal variations. Additionally, some tools are proprietary or no longer supported, which makes them difficult for decision makers to adopt. Methods To address these issues, we have created an open-source, freely available modeling tool called the Population-Aware Capacity Estimator for Human Resources for Health (PACE-HRH). The modeling platform has two components: an Excel-based workbook for data input and scenario management, and a stochastic Monte Carlo simulation package and analysis pipeline written in R. PACE-HRH has a demographics model that projects future populations, a task time model that estimates workload from both variable responsibilities and overhead, an optional seasonality model, and an optional cadre allocation model. Results To establish the utility of PACE-HRH, we run a demonstrative model based on a subset of eight clinical service categories, populated with Ethiopian data. The projections show an increase in weekly workload for a baseline population from 37.8 (36.0, 39.7) hours in 2021 to 44.0 (37.9, 49.8) hours in 2035. The ability to calculate a confidence interval is unique to PACE-HRH, as is the option to calculate the monthly variation in workload, which in this case amounts to seasonal amplitude of 6.8%. These results are demonstrative only and more curated input assumptions would be needed in order for the results to support decision making. Conclusions Modeling HRH requirements is valuable to planning processes. The PACE-HRH modeling package takes a novel approach to generating these estimates and is designed to be an easy-to-use platform that reduces barriers to use. There is a shortage of observational data on task times, which are key model assumptions, and time and motion studies are needed. However, even without improved data, PACE-HRH is an advancement in the field of HRH modeling and can be used to support evidence-based planning processes.

Background: The case count for coronavirus disease 2019 (COVID-19) is the predominant measure used to track epidemiological dynamics and inform policy decision-making. Case counts, however, are influenced by testing rates and strategies, which have varied over time and space. A method to interpret COVID-19 case counts consistently in the context of other surveillance data is needed, especially for data-limited settings in low- and middle-income countries (LMICs). Methods: Statistical analyses were used to detect changes in COVID-19 surveillance data. The pruned exact linear time change detection method was applied for COVID-19 case counts, number of tests, and test positivity rate over time. With this information, change points were categorized as likely driven by epidemiological dynamics or non-epidemiological influences, such as noise. Findings: Higher rates of epidemiological change detection are more associated with open testing policies than with higher testing rates. This study quantified alignment of non-pharmaceutical interventions with epidemiological changes. LMICs have the testing capacity to measure prevalence with precision if they use randomized testing. Rwanda stands out as a country with an efficient COVID-19 surveillance system. Subnational data reveal heterogeneity in epidemiological dynamics and surveillance. Interpretation: Relying solely on case counts to interpret pandemic dynamics has important limitations. Normalizing counts by testing rate mitigates some of these limitations, and an open testing policy is key to efficient surveillance. The study findings can be leveraged by public health officials to strengthen COVID-19 surveillance and support programmatic decision-making.

The first case of COVID-19 in sub-Saharan Africa (SSA) was reported by Nigeria on February 27, 2020. Whereas case counts in the entire region remain considerably less than those being reported by individual countries in Europe, Asia, and the Americas, variation in preparedness and response capacity as well as in data availability has raised concerns about undetected transmission events in the SSA region. To capture epidemiological details related to early transmission events into and within countries, a line list was developed from publicly available data on institutional websites, situation reports, press releases, and social media accounts. The availability of indicators—gender, age, travel history, date of arrival in country, reporting date of confirmation, and how detected—for each imported case was assessed. We evaluated the relationship between the time to first reported importation and the Global Health Security Index (GHSI) overall score; 13,201 confirmed cases of COVID-19 were reported by 48 countries in SSA during the 54 days following the first known introduction to the region. Of the 2,516 cases for which travel history information was publicly available, 1,129 (44.9%) were considered importation events. Imported cases tended to be male (65.0%), with a median age of 41.0 years (range: 6 weeks–88 years; IQR: 31–54 years). A country’s time to report its first importation was not related to the GHSI overall score, after controlling for air traffic. Countries in SSA generally reported with less publicly available detail over time and tended to have greater information on imported than local cases.

Background: As SARS-CoV-2 spread in early 2020, uncertainty about the scope, duration, and impact of the unfolding outbreaks caused numerous countries to interrupt many routine activities, including health services. Because immunization is an essential health service, modeling changes in SARS-CoV-2 infections among communities and health workers due to different vaccination activities was undertaken to understand the risks and to inform approaches to resume services. Methods: Agent-based modeling examined the impact of Supplemental Immunization Activities (SIAs) delivery strategies on SARS-CoV-2 transmission in communities and health workers for six countries capturing various demographic profiles and health system performance: Angola, Ecuador, Lao PDR, Nepal, Pakistan, and Ukraine. Results: Urban, fixed-post SIAs during periods of high SARS-CoV-2 prevalence increased infections within the community by around 28 [range:0-79] per 1000 vaccinations. House-to-house SIAs in mixed urban and rural contexts may import infections into previously naïve communities. Infections are elevated by around 60 [range:0-230] per 1000 vaccinations, but outcomes are sensitive to prevalence in health workers and SIA timing relative to peak. Conclusions: Incremental increases in SARS-CoV-2 infection due to SIAs was small and in proportion to overall prevalence. Younger populations experience lower transmission intensity and fewer excess infections per childhood vaccine delivered. Large rural populations have lower transmission intensity but face a greater risk of introduction of SARS-CoV-2 during an SIA.

IntroductionThe typhoid conjugate vaccine is a safe and effective method for preventing Salmonella enterica serovar Typhi (typhoid) and the WHO’s guidance supports its use in locations with ongoing transmission. However, many countries lack a robust clinical surveillance system, making it challenging to determine where to use the vaccine. Environmental surveillance is an alternative approach to identify ongoing transmission, but the cost to implement such a strategy is previously unknown.MethodsWe estimated the cost of an environmental surveillance program for thirteen potential protocols. Unit costs were obtained from research labs involved in protocol development and equipment information was obtained from manufacturers and expert opinion. The cost model includes thirteen components and twenty-seven pieces of equipment. We used Monte Carlo simulations to project total costs.ResultsTotal costs per sample including setup, overhead, and operational costs, range from $357–794 at a scale of 25 sites to $116–532 at 125 sites. Operational costs (ongoing expenditures) range from $218–584 per sample (25 sites) to $74–421 (125 sites). Eleven of the thirteen protocols have operational costs below $200 at this higher scale. Protocols with high up-front equipment costs benefit more from scale efficiencies. Sensitivity analyses show that laboratory labor, process efficiency, and the cost of consumables are the primary drivers of cost.ConclusionAt scale, environmental surveillance may be affordable, depending on the protocol chosen and the geographic context. Costs will need to be considered along with protocol sensitivity. Opportunities to leverage existing infrastructure and multi-disease platforms may be necessary to further reduce costs.Summary BoxWhat is already known?Environmental surveillance has been used for polio transmission surveillance to target vaccination campaigns to prevent outbreaks. Similarly, methods for typhoid environmental surveillance are being developed, and could be used to support vaccine introduction decisions, if they are accurate and affordable.What are the new findings?Across the scenarios examined, operational costs are between $74–584 per sample depending on the scale and protocol selected. Operational costs are 67–79% of total annualized costs across the same permutations.What do the new findings imply?Policymakers can use these estimates and understanding of the efficiency of scale benefits in order to design a surveillance system that balances total cost with surveillance sensitivity and geographic coverage.

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We performed this analysis using Covasim, an open-source agent-based model, which was calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we found that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.

0.1BackgroundCOVID-19 case counts are the predominant measure used to track epidemiological dynamics and inform policy decision-making. Case counts, however, are influenced by testing rates and strategies, which have varied over time and space. A method to consistently interpret COVID-19 case counts in the context of other surveillance data is needed, especially for data-limited settings in low- and middle-income countries (LMICs).0.2MethodsWe leverage statistical analyses to detect changes in COVID-19 surveillance data. We apply the pruned exact linear time change detection method for COVID-19 case counts, number of tests, and test positivity rate over time. With this information, we categorize change points as likely driven by epidemiological dynamics or non-epidemiological influences such as noise.0.3FindingsHigher rates of epidemiological change detection are more associated with open testing policies than with higher testing rates. We quantify alignment of non-pharmaceutical interventions with epidemiological changes. LMICs have the testing capacity to measure prevalence with precision if they use randomized testing. Rwanda stands out as a country with an efficient COVID-19 surveillance system. Sub-national data reveal heterogeneity in epidemiological dynamics and surveillance.0.4InterpretationRelying solely on case counts to interpret pandemic dynamics has important limitations. Normalizing counts by testing rate mitigates some of these limitations, and open testing policy is key to efficient surveillance. Our findings can be leveraged by public health officials to strengthen COVID-19 surveillance and support programmatic decision-making.0.5FundingThis publication is based on models and data analysis performed by the Institute for Disease Modeling at the Bill & Melinda Gates Foundation.Research in ContextEvidence before this studyWe searched for articles on the current practices, challenges, and proposals for COVID-19 surveillance in LMICs. We used Google Scholar with search terms including “COVID surveillance.” Existing studies were found to be qualitative, anecdotal, or highly location-specific.Added value of this studyWe developed a quantitative method that makes use of limited information available from LMICs. Our approach improves interpretation of epidemiological data and enables evaluation of COVID-19 surveillance dynamics across countries.Implications of all the available evidenceOur results demonstrate the importance of open testing for strong surveillance systems, bolstering existing anecdotal evidence. We show strong alignment across LMICs between non-pharmaceutical interventions and epidemiological changes. We demonstrate the importance of considering sub-national heterogeneity of epidemiological dynamics and surveillance.

Highlights • Countries within the sub-Saharan Africa (SSA) region may experience high COVID-19 case fatality rates given limited healthcare capacity to manage critical cases and poor detection leading to delayed care-seeking. • Over the first months of the epidemic in SSA and consistent with findings from other countries globally, deceased cases tended to be majority male, over the age of 50, and have underlying comorbidities. • Delayed or no care-seeking was prevalent among deceased COVID-19 cases in Burkina Faso. • Analysis on a synthetic case population representative of demographic and health characteristics of Burkina Faso COVID-19 patients suggested that treatment with oxygen therapy or convalescent plasma reduced the odds of mortality, after adjusting for age, sex, and presence of underlying comorbid conditions. • Low-cost, scalable and sustainable strategies for COVID-19 case management in the SSA context warrant attention and investment to reduce disparity in case fatality., Background Absolute numbers of COVID-19 cases and deaths reported to date in the sub-Saharan Africa (SSA) region have been significantly lower than those across the Americas, Asia, and Europe. As a result, there has been limited information about the demographic and clinical characteristics of deceased cases in the region, as well as the impacts of different case management strategies. Methods Data from deceased cases reported across SSA through May 10, 2020 and from hospitalized cases in Burkina Faso through April 15, 2020 were analyzed. Demographic, epidemiological, and clinical information on deceased cases in SSA was derived through a line-list of publicly available information and, for cases in Burkina Faso, from aggregate records at the Centre Hospitalier Universitaire de Tengandogo in Ouagadougou. A synthetic case population was derived probabilistically using distributions of age, sex, and underlying conditions from populations of West African countries to assess individual risk factors and treatment effect sizes. Logistic regression analysis was conducted to evaluate the adjusted odds of survival for patients receiving oxygen therapy or convalescent plasma, based on therapeutic effectiveness observed for other respiratory illnesses. Results Across SSA, deceased cases for which demographic data are available have been predominantly male (63/103, 61.2%) and over 50 years of age (59/75, 78.7%). In Burkina Faso, specifically, the majority of deceased cases either did not seek care at all or were hospitalized for a single day (59.4%, 19/32); hypertension and diabetes were often reported as underlying conditions. After adjustment for sex, age, and underlying conditions in the synthetic case population, the odds of mortality for cases not receiving oxygen therapy was significantly higher than those receiving oxygen, such as due to disruptions to standard care (OR: 2.07; 95% CI: 1.56 – 2.75). Cases receiving convalescent plasma had 50% reduced odds of mortality than those who did not (95% CI: 0.24 – 0.93). Conclusions Investment in sustainable production and maintenance of supplies for oxygen therapy, along with messaging around early and appropriate use for healthcare providers, caregivers, and patients could reduce COVID-19 deaths in SSA. Further investigation into convalescent plasma is warranted, as data on its effectiveness specifically in treating COVID-19 becomes available. The success of supportive or curative clinical interventions will depend on earlier treatment seeking, such that community engagement and risk communication will be critical components of the response.

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required., Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures, with enormous societal and economic costs. Here, the authors demonstrate the feasibility of a test-trace-quarantine strategy using an agent-based model and detailed data on the Seattle region.

BackgroundAbsolute numbers of COVID-19 cases and deaths reported to date in the sub-Saharan Africa (SSA) region have been significantly lower than those across the Americas, Asia, and Europe. As a result, there has been limited information about the demographic and clinical characteristics of deceased cases in the region, as well as the impacts of different case management strategies.MethodsData from deceased cases reported across SSA through May 10, 2020 and from hospitalized cases in Burkina Faso through April 15, 2020 were analyzed. Demographic, epidemiological, and clinical information on deceased cases in SSA was derived through a line-list of publicly available information and, for cases in Burkina Faso, from aggregate records at the Center Hospitalier Universitaire de Tengandogo in Ouagadougou. A synthetic case population was derived probabilistically using distributions of age, sex, and underlying conditions from populations of West African countries to assess individual risk factors and treatment effect sizes. Logistic regression analysis was conducted to evaluate the adjusted odds of survival for patients receiving oxygen therapy or convalescent plasma, based on therapeutic effectiveness observed for other respiratory illnesses.ResultsAcross SSA, deceased cases for which demographic data are available have been predominantly male (63/103, 61.2%) and over 50 years of age (59/75, 78.7%). In Burkina Faso, specifically, the majority of deceased cases either did not seek care at all or were hospitalized for a single day (59.4%, 19/32); hypertension and diabetes were often reported as underlying conditions. After adjustment for sex, age, and underlying conditions in the synthetic case population, the odds of mortality for cases not receiving oxygen therapy was significantly higher than those receiving oxygen, such as due to disruptions to standard care (OR: 2.07; 95% CI: 1.56 – 2.75). Cases receiving convalescent plasma had 50% reduced odds of mortality than those who did not (95% CI: 0.24 – 0.93).ConclusionInvestment in sustainable production and maintenance of supplies for oxygen therapy, along with messaging around early and appropriate use for healthcare providers, caregivers, and patients could reduce COVID-19 deaths in SSA. Further investigation into convalescent plasma is warranted, as data on its effectiveness specifically in treating COVID-19 becomes available. The success of supportive or curative clinical interventions will depend on earlier treatment seeking, such that community engagement and risk communication will be critical components of the response.

BackgroundThe first case of COVID-19 in sub-Saharan Africa (SSA) was reported by Nigeria on February 27, 2020. While case counts in the entire region remain considerably less than those being reported by individual countries in Europe, Asia, and the Americas, SSA countries remain vulnerable to COVID morbidity and mortality due to systemic healthcare weaknesses, less financial resources and infrastructure to address the new crisis, and untreated comorbidities. Variation in preparedness and response capacity as well as in data availability has raised concerns about undetected transmission events.MethodsConfirmed cases reported by SSA countries were line-listed to capture epidemiological details related to early transmission events into and within countries. Data were retrieved from publicly available sources, including institutional websites, situation reports, press releases, and social media accounts, with supplementary details obtained from news articles. A data availability score was calculated for each imported case in terms of how many indicators (sex, age, travel history, date of arrival in country, reporting date of confirmation, and how detected) could be identified. We assessed the relationship between time to first importation and overall Global Health Security Index (GHSI) using Cox regression. K-means clustering grouped countries according to healthcare capacity and health and demographic risk factors.ResultsA total of 13,201 confirmed cases of COVID-19 were reported by 48 countries in SSA during the 54 days following the first known introduction to the region. Out of the 2516 cases for which travel history information was publicly available, 1129 (44.9%) were considered importation events. At the regional level, imported cases tended to be male (65.0%), were a median 41.0 years old (Range: 6 weeks - 88 years), and most frequently had recent travel history from Europe (53.1%). The median time to reporting an introduction was 19 days; a country’s time to report its first importation was not related to GHSI, after controlling for air traffic. Countries that had, on average, the highest case fatality rates, lowest healthcare capacity, and highest probability of premature death due to non-communicable diseases were among the last to report any cases.ConclusionsCountries with systemic, demographic, and pre-existing health vulnerabilities to severe COVID-related morbidity and mortality are less likely to report any cases or may be reporting with limited public availability of information. Reporting on COVID detection and response efforts, as well as on trends in non-COVID illness and care-seeking behavior, is critical to assessing direct and indirect consequences and capacity needs in resource-constrained settings. Such assessments aid in the ability to make data-driven decisions about interventions, country priorities, and risk assessment.Key MessagesWe line-listed epidemiological indicators for the initial cases reported by 48 countries in sub-Saharan Africa by reviewing and synthesizing information provided by official institutional outlets and news sources.Our findings suggest that countries with the largest proportions of untreated comorbidities, as measured by probability of premature death due to non-communicable diseases, and the fewest healthcare resources tended to not be reporting any cases at one-month post-introduction into the region.Using data availability as a measure of gaps in detection and reporting and relating them to COVID-specific parameters for morbidity and mortality provides a measure of vulnerability.Accurate and available information on initial cases in seeding local outbreaks is key to projecting case counts and assessing the potential impact of intervention approaches, such that support for local data teams will be important as countries make decisions about control strategies.

Despite increased efforts and spending toward polio eradication, it has yet to be eliminated worldwide. We aimed to project economic costs of polio eradication compared to permanent control. We used historical Financial Resource Requirements from the Global Polio Eradication Initiative, as well as vaccination and population data from publicly available sources, to project costs for routine immunization, immunization campaigns, surveillance and laboratory resources, technical assistance, social mobilization, treatment, and overhead. We found that cumulative spending for a control strategy would exceed that for an eradication strategy in 2032 (range, 2027–2051). Eradication of polio would likely be cost-saving compared to permanent control.

Typhoid conjugate vaccine (TCV) has been shown to be costeffective in some settings, but to make the most of limited healthcare funds, the WHO recommends that decisions about when and where to introduce TCV should be based on local evidence of transmission. Further, Gavi, The Vaccine Alliance, recommends that countries request TCV funding based on epidemiological data from within-country. Since few countries have reliable case reporting, we considered environmental surveillance as a decision-support tool for vaccine introduction choices. Environmental surveillance methods are still in development and this analysis assesses its potential as a decision support tool. We combined a disease transmission simulation model with an economic model, to quantify the value of surveillance. The disease transmission model is fit to historical case reporting from Blantyre, Malawi. The economic model takes a social perspective and incorporates the cost of environmental surveillance, vaccination, and both direct and indirect cost of care for acute typhoid cases. We find that the use of time-limited environmental surveillance to differentiate low- from high-endemicity areas is low cost relative to potential savings, especially if it is paired with a locally targeted vaccination campaign. These findings are robust to the uncertainty of cost model parameters and underlying endemicity.

This analysis examined how polio program costs vary with scale for vaccination and disease surveillance, based on historical budget data published by the Global Polio Eradication Initiative (GPEI) from 2005 to 2018. We applied a linear mixed effects regression model in order to understand the cost structure of the historical GPEI budgets, with the goal that lessons learned from polio may be extended to other global disease elimination programs. Our findings demonstrate that there are economies of scale for vaccine delivery operations and for disease surveillance, which means that larger programs can leverage fixed costs and achieve better cost-efficiency as they scale. This finding should enable decision makers to create more reliable budgets, which support fundraising and optimal resource allocation. They also provide insight into how cost effectiveness changes as programs scale up during progressive disease control and elimination, as well as what level of resources are needed to sustain a program that is scaling back post-eradication and through to certification.

Background Measles causes significant childhood morbidity in Nigeria. Routine immunization (RI) coverage is around 40% country-wide, with very high levels of spatial heterogeneity (3–86%), with supplemental immunization activities (SIAs) at 2-year or 3-year intervals. We investigated cost savings and burden reduction that could be achieved by adjusting the inter-campaign interval by region. Methods We modeled 81 scenarios; permuting SIA calendars of every one, two, or three years in each of four regions of Nigeria (North-west, North-central, North-east, and South). We used an agent-based disease transmission model to estimate the number of measles cases and ingredients-based cost models to estimate RI and SIA costs for each scenario over a 10 year period. Results Decreasing SIAs to every three years in the North-central and South (regions of above national-average RI coverage) while increasing to every year in either the North-east or North-west (regions of below national-average RI coverage) would avert measles cases (0.4 or 1.4 million, respectively), and save vaccination costs (save $19.4 or $5.4 million, respectively), compared to a base-case of national SIAs every two years. Decreasing SIA frequency to every three years in the South while increasing to every year in the just the North-west, or in all Northern regions would prevent more cases (2.1 or 5.0 million, respectively), but would increase vaccination costs (add $3.5 million or $34.6 million, respectively), for $1.65 or $6.99 per case averted, respectively. Conclusions Our modeling shows how increasing SIA frequency in Northern regions, where RI is low and birth rates are high, while decreasing frequency in the South of Nigeria would reduce the number of measles cases with relatively little or no increase in vaccination costs. A national vaccination strategy that incorporates regional SIA targeting in contexts with a high level of sub-national variation would lead to improved health outcomes and/or lower costs.

Measles vaccination is a cost-effective way to prevent infection and reduce mortality and morbidity. However, in countries with fragile routine immunization infrastructure, coverage rates are still low and supplementary immunization campaigns (SIAs) are used to reach previously unvaccinated children. During campaigns, vaccine is generally administered to every child, regardless of their vaccination status and as a result, there is the possibility that a child that is already immune to measles (i.e. who has had 2+ vaccinations) would receive an unnecessary dose, resulting in excess cost. Selective vaccination has been proposed as one solution to this; children who were able to provide documentation of previous vaccination would not be vaccinated repeatedly. While this would result in reduced vaccine and supply cost, it would also require additional staff time and increased social mobilization investment, potentially outweighing the benefits. We utilize Monte Carlo simulation to assess under what conditions a selective vaccination policy would indeed result in net savings. We demonstrate that cost savings are possible in contexts with a high joint probability of an individual child having both 2+ previous measles doses and also an available record. We also find that the magnitude of net cost savings is highly dependent on whether a country is using measles-only or measles-rubella vaccine and on the required skill set of the individual who would review the previous vaccination records.

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America., Author summary Mathematical models have played an important role in helping countries around the world decide how to best tackle the COVID-19 pandemic. In this paper, we describe a COVID-19 model, called Covasim (COVID-19 Agent-based Simulator), that we developed to help answer these questions. Covasim can be tailored to the local context by using detailed data on the population (such as the population age distribution and number of contacts between people) and the epidemic (such as diagnosed cases and reported deaths). While Covasim can be used to explore theoretical research questions or to make projections, its main purpose is to evaluate the effect of different interventions on the epidemic. These interventions include physical interventions (mobility restrictions and masks), diagnostic interventions (testing, contact tracing, and quarantine), and pharmaceutical interventions (vaccination). Covasim is open-source, written in Python, and comes with extensive documentation, tutorials, and a webapp to ensure it can be used as easily and broadly as possible. In partnership with local stakeholders, Covasim has been used to answer policy and research questions in more than a dozen countries, including India, the United States, Vietnam, and Australia.

Introduction The lack of specific policies on how many children must be present at a vaccinating location before a healthcare worker can open a measles-containing vaccine (MCV) – i.e. the vial-opening threshold – has led to inconsistent practices, which can have wide-ranging systems effects. Methods Using HERMES-generated simulation models of the routine immunization supply chains of Benin, Mozambique and Niger, we evaluated the impact of different vial-opening thresholds (none, 30% of doses must be used, 60%) and MCV presentations (10-dose, 5-dose) on each supply chain. We linked these outputs to a clinical- and economic-outcomes model which translated the change in vaccine availability to associated infections, medical costs, and DALYs. We calculated the economic impact of each policy from the health system perspective. Results The vial-opening threshold that maximizes vaccine availability while minimizing costs varies between individual countries. In Benin (median session size = 5), implementing a 30% vial-opening threshold and tailoring distribution of 10-dose and 5-dose MCVs to clinics based on session size is the most cost-effective policy, preventing 671 DALYs ($471/DALY averted) compared to baseline (no threshold, 10-dose MCVs). In Niger (median MCV session size = 9), setting a 60% vial-opening threshold and tailoring MCV presentations is the most cost-effective policy, preventing 2897 DALYs ($16.05/ DALY averted). In Mozambique (median session size = 3), setting a 30% vial-opening threshold using 10-dose MCVs is the only beneficial policy compared to baseline, preventing 3081 DALYs ($85.98/DALY averted). Across all three countries, however, a 30% vial-opening threshold using 10-dose MCVs everywhere is the only MCV threshold that consistently benefits each system compared to baseline. Conclusion While the ideal vial-opening threshold policy for MCV varies by supply chain, implementing a 30% vial-opening threshold for 10-dose MCVs benefits each system by improving overall vaccine availability and reducing associated medical costs and DALYs compared to no threshold.