Seasonally Dependent Relationships between Indicators of Malaria Transmission and Disease Provided by Mathematical Model Simulations

Abstract

Evaluating the effectiveness of malaria control interventions on the basis of their impact on transmission as well as impact on morbidity and mortality is becoming increasingly important as countries consider pre-elimination and elimination as well as disease control. Data on prevalence and transmission are traditionally obtained through resource-intensive epidemiological and entomological surveys that become difficult as transmission decreases. This work employs mathematical modeling to examine the relationships between malaria indicators allowing more easily measured data, such as routine health systems data on case incidence, to be translated into measures of transmission and other malaria indicators. Simulations of scenarios with different levels of malaria transmission, patterns of seasonality and access to treatment were run with an ensemble of models of malaria epidemiology and within-host dynamics, as part of the OpenMalaria modeling platform. For a given seasonality profile, regression analysis mapped simulation results of malaria indicators, such as annual average entomological inoculation rate, prevalence, incidence of uncomplicated and severe episodes, and mortality, to an expected range of values of any of the other indicators. Results were validated by comparing simulated relationships between indicators with previously published data on these same indicators as observed in malaria endemic areas. These results allow for direct comparisons of malaria transmission intensity estimates made using data collected with different methods on different indicators. They also address key concerns with traditional methods of quantifying transmission in areas of differing transmission intensity and sparse data. Although seasonality of transmission is often ignored in data compilations, the models suggest it can be critically important in determining the relationship between transmission and disease. Application of these models could help public health officials detect changes of disease dynamics in a population and plan and assess the impact of malaria control interventions.

Temporal disaggregation of annual dengue cases to monthly dengue cases

“…Environmental risk to dengue, or any mosquito-borne disease, is related to the parameters of precipitation, temperature, land cover, and vegetation health, all of which affect the habituation of mosquitoes (Chitnis et al 2012;Stuckey et al 2014;Parham et al 2015;Siraj et al 2015). Monthly records of these parameters were obtained from remotely sensed data (Table 2), with Normalized Difference Vegetation Index (NDVI) considered a measure of vegetation health.…”

Mapping malaria seasonality in Madagascar using health facility data

“…By comparing the values of the fitted curve, the major and the minor peaks of a bimodal distribution can be identified. Although an arbitrary number of von Mises components can be used, one or two were used because areas with seasonal malaria transmission typically have one or two main seasons [2].…”

“…That is, malaria burden, which can be measured by metrics including parasite prevalence or the number of clinical cases, follows an annually recurring seasonal pattern that is typically attributed to the relationship of the mosquito vector and parasite life cycles with the environment. The rationale for developing methods capable of enumerating location-specific seasonal characteristics is to assist planning for intervention distributions to improve their efficacy, develop early warning systems, and improve the temporal resolution and overall accuracy of malaria burden estimation models [2].…”

New and Enhanced Tools for Civil Military Operations (NET-CMO)

“…Environmental risk to dengue, or any mosquito-borne disease, is related to the parameters of precipitation, temperature, land cover, and vegetation health, all of which affect the habituation of mosquitoes (Chitnis et al 2012;Stuckey et al 2014;Parham et al 2015;Siraj et al 2015). Monthly records of these parameters were obtained from remotely sensed data (Table 6), with NDVI considered a measure of vegetation health.…”