Hitting a Moving Target: A Model for Malaria Elimination in the Presence of Population Movement

South Africa is committed to eliminating malaria with a goal of zero local transmission by 2018. Malaria elimination strategies may be unsuccessful if they focus only on vector biology, and ignore the mobility patterns of humans, particularly where the majority of infections are imported. In the first study in Mpumalanga Province in South Africa designed for this purpose, a metapopulation model is developed to assess the impact of their proposed elimination-focused policy interventions. A stochastic, non-linear, ordinary-differential equation model is fitted to malaria data from Mpumalanga and neighbouring Maputo Province in Mozambique. Further scaling-up of vector control is predicted to lead to a minimal reduction in local infections, while mass drug administration and focal screening and treatment at the Mpumalanga-Maputo border are predicted to have only a short-lived impact. Source reduction in Maputo Province is predicted to generate large reductions in local infections through stemming imported infections. The mathematical model predicts malaria elimination to be possible only when imported infections are treated before entry or eliminated at the source suggesting that a regionally focused strategy appears needed, for achieving malaria elimination in Mpumalanga and South Africa

Exploring the seasonality of reported treated malaria cases in Mpumalanga, South Africa

South Africa, having met the World Health Organisation's pre-elimination criteria, has set a goal to achieve malaria elimination by 2018. Mpumalanga, one of three provinces where malaria transmission still occurs, has a malaria season subject to unstable transmission that is prone to sporadic outbreaks. As South Africa prepares to intensify efforts towards malaria elimination, there is a need to understand patterns in malaria transmission so that efforts may be targeted appropriately. This paper describes the seasonality of transmission by exploring the relationship between malaria cases and three potential drivers: rainfall, geography (physical location) and the source of infection (local/imported). Seasonal decomposition of the time series by Locally estimated scatterplot smoothing is applied to the case data for the geographical and source of infection sub-groups. The relationship between cases and rainfall is assessed using a cross-correlation analysis. The malaria season was found to have a short period of no/low level of reported cases and a triple peak in reported cases between September and May; the three peaks occurring in October, January and May. The seasonal pattern of locally-sourced infection mimics the triple-peak characteristic of the total series while imported infections contribute mostly to the second and third peak of the season (Christmas and Easter respectively). Geographically, Bushbuckridge municipality, which exhibits a different pattern of cases, contributed mostly to the first and second peaks in cases while Maputo province (Mozambique) experienced a similar pattern in transmission to the imported cases. Though rainfall lagged at 4 weeks was significantly correlated with malaria cases, this effect was dampened due to the growing proportion of imported cases since 2006. These findings may be useful as they enhance the understanding of the current incidence pattern and may inform mathematical models that enable one to predict the impact changes in these drivers will have on malaria transmission

An interactive application for malaria elimination transmission and costing in the Asia-Pacific

Leaders in the Asia-Pacific have endorsed an ambitious target to eliminate malaria in the region by 2030. The emergence and spread of artemisinin drug resistance in the Greater Mekong Subregion makes elimination urgent and strategic for the global goal of malaria eradication. Mathematical modelling is a useful tool for assessing and comparing different elimination strategies and scenarios to inform policymakers. Mathematical models are especially relevant in this context because of the wide heterogeneity of regional, country and local settings, which means that different strategies are needed to eliminate malaria. However, models and their predictions can be seen as highly technical, limiting their use for decision making. Simplified applications of models are needed to allow policy makers to benefit from these valuable tools. This paper describes a method for communicating complex model results with a user-friendly and intuitive framework. Using open-source technologies, we designed and developed an interactive application to disseminate the modelling results for malaria elimination. The design was iteratively improved while the application was being piloted and extensively tested by a diverse range of researchers and decision makers. This application allows several target audiences to explore, navigate and visualise complex datasets and models generated in the context of malaria elimination. It allows widespread access, use of and interpretation of models, generated at great effort and expense as well as enabling them to remain relevant for a longer period of time. It has long been acknowledged that scientific results need to be repackaged for larger audiences. We demonstrate that modellers can include applications as part of the dissemination strategy of their findings. We highlight that there is a need for additional research in order to provide guidelines and direction for designing and developing effective applications for disseminating models

Malaria elimination transmission and costing in the Asia-Pacific: developing an investment case

Background:; The Asia-Pacific region has made significant progress against malaria, reducing cases and deaths by over 50% between 2010 and 2015. These gains have been facilitated in part, by strong political and financial commitment of governments and donors. However, funding gaps and persistent health system challenges threaten further progress. Achieving the regional goal of malaria elimination by 2030 will require an intensification of efforts and a plan for sustainable financing. This article presents an investment case for malaria elimination to facilitate these efforts.; Methods:; A transmission model was developed to project rates of decline of; Plasmodium falciparum; and; Plasmodium vivax; malaria and the output was used to determine the cost of the interventions that would be needed for elimination by 2030. In total, 80 scenarios were modelled under various assumptions of resistance and intervention coverage. The mortality and morbidity averted were estimated and health benefits were monetized by calculating the averted cost to the health system, individual households, and society. The full-income approach was used to estimate the economic impact of lost productivity due to premature death and illness, and a return on investment was computed.; Results; : The study estimated that malaria elimination in the region by 2030 could be achieved at a cost of USD 29.02 billion (range: USD 23.65-36.23 billion) between 2017 and 2030. Elimination would save over 400,000 lives and avert 123 million malaria cases, translating to almost USD 90 billion in economic benefits. Discontinuing vector control interventions and reducing treatment coverage rates to 50% will result in an additional 845 million cases, 3.5 million deaths, and excess costs of USD 7 billion. Malaria elimination provides a 6:1 return on investment.; Conclusion:; This investment case provides compelling evidence for the benefits of continued prioritization of funding for malaria and can be used to develop an advocacy strategy

The full value of vaccine assessments concept - current opportunities and recommendations

For vaccine development and adoption decisions, the ‘Full Value of Vaccine Assessment’ (FVVA) framework has been proposed by the WHO to expand the range of evidence available to support the prioritization of candidate vaccines for investment and eventual uptake by low- and middle-income countries. Recent applications of the FVVA framework have already shown benefits. Building on the success of these applications, we see important new opportunities to maximize the future utility of FVVAs to country and global stakeholders and provide a proof-of-concept for analyses in other areas of disease control and prevention. These opportunities include the following: (1) FVVA producers should aim to create evidence that explicitly meets the needs of multiple key FVVA consumers, (2) the WHO and other key stakeholders should develop standardized methodologies for FVVAs, as well as guidance for how different stakeholders can explicitly reflect their values within the FVVA framework, and (3) the WHO should convene experts to further develop and prioritize the research agenda for outcomes and benefits relevant to the FVVA and elucidate methodological approaches and opportunities for standardization not only for less well-established benefits, but also for any relevant research gaps. We encourage FVVA stakeholders to engage with these opportunities

The Full Value of Vaccine Assessments Concept—Current Opportunities and Recommendations

For vaccine development and adoption decisions, the ‘Full Value of Vaccine Assessment’ (FVVA) framework has been proposed by the WHO to expand the range of evidence available to support the prioritization of candidate vaccines for investment and eventual uptake by low- and middle-income countries. Recent applications of the FVVA framework have already shown benefits. Building on the success of these applications, we see important new opportunities to maximize the future utility of FVVAs to country and global stakeholders and provide a proof-of-concept for analyses in other areas of disease control and prevention. These opportunities include the following: (1) FVVA producers should aim to create evidence that explicitly meets the needs of multiple key FVVA consumers, (2) the WHO and other key stakeholders should develop standardized methodologies for FVVAs, as well as guidance for how different stakeholders can explicitly reflect their values within the FVVA framework, and (3) the WHO should convene experts to further develop and prioritize the research agenda for outcomes and benefits relevant to the FVVA and elucidate methodological approaches and opportunities for standardization not only for less well-established benefits, but also for any relevant research gaps. We encourage FVVA stakeholders to engage with these opportunities

A mathematical modelling approach for the elimination of malaria in Mpumalanga, South Africa

Mpumalanga in South Africa is committed to eliminating malaria by 2018 and efforts are increasing beyond that necessary for malaria control. The eastern border of Mpumalanga is most affected by malaria with imported cases in Mpumalanga overtaking local cases in recent years. Mathematical modelling may be used to study the incidence and spread of disease with an important benefit being the ability to enact exogenous change on the system to predict impact without committing any real resources. Three models are developed to simulate malaria transmission: (1) a deterministic non-linear ordinary differential equation model, (2) a stochastic non-linear metapopulation differential equation model and (3) a stochastic hybrid metapopulation differential equation, individual-based model. These models are fitted to weekly case data from Mpumalanga from 2002 to 2008, and validated with data from 2009 to 2012. Interventions such as scaling-up vector control, mass drug administration, focal screen and treat campaign at the Mpumalanga-Maputo border-control point and source reduction are applied to the model to assess their potential impact on transmission and whether they may be used alone or in combination to achieve malaria elimination. The models predicted that scaling up vector control results in substantial decreases in local infections, though with little impact on imported infections. Mass drug administration is a high impacting but short-lived intervention with transmission reverting to pre-intervention levels within three years. Focal screen and treat campaigns are predicted to result in substantial decreases in local infections, though success of the campaign is dependent on the ability to detect low parasitemic infections. Large decreases in local infections are also predicted to be achieved through foreign source reduction. The impact of imported infections is such that malaria elimination is only predicted if all imported infections are treated before entry into Mpumalanga, or are themselves eliminated at their source. Thus a regionally-focused strategy may stand a better chance at achieving elimination in Mpumalanga and South Africa compared to a nationally-focused one. In this manner, mathematical models may form an integral part of the research, planning and evaluation of the research, planning and evaluation of elimination-focused strategies so that malaria elimination is possible in the foreseeable future

A simulation model of antimalarial drug resistance

Includes bibliographical references (leaves 132-137).Malaria ranks among the world's most important tropical parasitic diseases with world prevalence figures between 350 and 550 million clinical cases per annum. [WHO, 2008a] 'Treatment and prevention of malaria places a considerable burden on struggling economies where the disease is rampant. Research in malaria does not stop as the change in response to antimalarial drug treatment requires the development of new drugs and innovation in the use of old drugs. This thesis focused on building a model of the spread of resistance to Sulfadoxine/Pyrimethamine (SP) in a setting where both SP and SP in artemisinin-based combination therapy (ACT) are the first line therapies for malaria. The model itself is suitable to any low transmission setting where antimalarial drug resistance exists but the country of choice in this modeling exercise was Mozambique. The model was calibrated using parameters specific to the malaria situation in Mozambique. This model was intended to be used to aid decision making in countries where antimalarial drug resistance exists to help prevent resistance spreading to such an extent that drugs lose their usefulness in curing malaria. The modeling technique of choice was differential equation modeling; a simulation technique that falls under the System Dynamics banner in the Operations Research armamentarium. It is a technique that allowed the modeling of stocks and flows that represent different stages or groupings in the disease process and the rate of movement between these stages respectively. The base model that was built allowed infected individuals to become infectious, to be treated with SP or ACT and to be sensitive to or fail treatment. Individuals were allowed a period of temporary immunity where they would not be reinfected until the residual SP had been eliminated from their bloodstream. The base model was then further developed to include the pharmacokinetic properties of SP where individuals were allowed to be reinfected with certain strains of infection given the level of residual drug in their bloodstream after their current infection had been cleared. The models used in this thesis were built with idea of expanding on previous models and using available data to improve parameter estimates. The model at its core is similar to the resistance model used in Koella and Antia [2003] where differential equation modeling was used to monitor a population as it became infected with a sensitive or resistant infection and then University of Cape Town recovered. The inclusion in the model of the PK component was derived from Prudhomme-O'Meara et al. [2006] where individuals could be reinfected depending on the residual drug in their bloodstream. Rather than modeling simply sensitive and resistant infections, mutations categories were used as was the case in Watkins et al. [2005] population genetics model. The use of mutation categories allowed one to use parameters specific to these categories rather than the sensitive/resistant stratification and this is particularly relevant in Mozambique where all mutation categories still exhibit some degree of sensitivity to treatment i.e. total resistance has not yet developed for any particular mutation category. The last adaptation of the model was to use gametocyte information directly to determine human infectiousness rather than through using a gametocyte switching rate (constant multiplier used to convert parasite density to gametocyte density) as was done in Pongtavompinyo [2006]. The models developed in this thesis found that the existing vector control and drug policy in Mozambique had the major effect of decreasing total prevalence of malaria by approximately 70% in the 11 year period. The distribution of Res3 (presence of DHFR triple) and Res5 (presence of DHFR triple and DHPS double) infections changed over the 11 year period with Res3 infections initially increasing and then decreasing while Res5 infections started low and increased to overtake Res3 infections. The timing of the change in this composition of infection corresponds with the introduction of ACT and thus it appears that the use of ACT prompted the increased prevalence of quintuple parasites over DHFR triple and sensitive parasites. The total number of failures decreased substantially after the introduction of ACT to 17% of its previous level. The results of the base model corresponded with the observed data from the SEACAT study in terms of the magnitude and the trends of the impact of the change to ACT policy, but underestimated the impact of the vector control strategies compared to rapid effect noted in Sharp et al. [2007]. The Scenario testing of the base model showed that vector control is an effective strategy to reduce prevalence and that it is sensitive to the time at which the control is started as it decreased prevalence very gradually. The Scenario testing of the base model also showed that the introduction of ACT in Mozambique had a greater impact on reducing prevalence and that the start time of the ACT strategy did not decrease the effect on prevalence though earlier start times decreased the total number of resistance cases. The ratio of Res5 to Res3 infections increased faster when ACT was the treatment policy than when SP was the policy. Thus higher values of this ratio are associated with ACT being the treatment strategy in place. Thus differential equation modeling is an effective modeling tool to capture the spread of disease and to test the effects of policy interventions as it allows one to assess these effects on populations and averages out individual-level intricacies to better inform policy decisions