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Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria

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posted on 05.08.2016, 11:00 by Freya M. Shearer, Zhi Huang, Daniel J. Weiss, Antoinette Wiebe, Harry S. Gibson, Katherine E. Battle, David M. Pigott, Oliver J. Brady, Chaturong Putaporntip, Somchai Jongwutiwes, Yee Ling Lau, Magnus Manske, Roberto Amato, Iqbal R. F. Elyazar, Indra Vythilingam, Samir Bhatt, Peter W. Gething, Balbir Singh, Nick Golding, Simon I. Hay, Catherine L. Moyes

Background

Infection by the simian malaria parasite, Plasmodium knowlesi, can lead to severe and fatal disease in humans, and is the most common cause of malaria in parts of Malaysia. Despite being a serious public health concern, the geographical distribution of P. knowlesi malaria risk is poorly understood because the parasite is often misidentified as one of the human malarias. Human cases have been confirmed in at least nine Southeast Asian countries, many of which are making progress towards eliminating the human malarias. Understanding the geographical distribution of P. knowlesi is important for identifying areas where malaria transmission will continue after the human malarias have been eliminated.

Methodology/Principal Findings

A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines).

Conclusions/Significance

We have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.

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