Mathematical model limits malaria outbreaks.

Mathematical models can effectively predict and track malaria transmission trends, ultimately quantifying the efficiency of various treatment and eradication strategies in high-risk regions. In a new paper by authors from Society for Industrial and Applied Mathematics explain a malaria transmission model that considers three distinct factors: climate, the extrinsic incubation period (EIP), and the vector-bias effect. One of the most common infectious diseases in the world, malaria causes public health problems and depresses the economy of infected areas. When untreated or treated improperly, the disease can result in fatalities. Despite impressive control measures and increased prevention techniques, which have reduced the global malaria mortality rate by 29% over the last six years, 3.3 billion people throughout 97 countries and territories still face a risk of infection. According to the World Health Organization, there were 212 million cases of malaria in 2015; approximately 429,000 resulted in death. Sub-Saharan Africa continues to exhibit a disproportionately high number of outbreaks and fatalities. Mathematical models can effectively predict and track malaria transmission trends, ultimately quantifying the efficiency of various treatment and eradication strategies in high-risk regions. In a paper publishing in the SIAM Journal on Applied Mathematics on January 24th, Xiunan Wang and Xiao-Qiang Zhao explain a malaria transmission model that considers three distinct factors: climate, the extrinsic incubation period (EIP), and the vector-bias effect. Using data from Maputo Province, Mozambique to simulate transmission trends, the authors ultimately present a possible way to limit the disease's transmission.