Identifying Plasmodium falciparum transmission patterns through parasite prevalence and entomological inoculation rate

Author:

Amoah Benjamin1ORCID,McCann Robert S234ORCID,Kabaghe Alinune N35,Mburu Monicah23,Chipeta Michael G367ORCID,Moraga Paula18,Gowelo Steven23,Tizifa Tinashe35,van den Berg Henk2ORCID,Mzilahowa Themba3,Takken Willem2,van Vugt Michele5,Phiri Kamija S3,Diggle Peter J1,Terlouw Dianne J369,Giorgi Emanuele1ORCID

Affiliation:

1. Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom

2. Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands

3. Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi

4. Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, United States

5. Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands

6. Malawi-Liverpool Wellcome Trust Research Programme, Blantyre, Malawi

7. Big Data Institute, University of Oxford, Oxford, United Kingdom

8. Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

9. Liverpool School of Tropical Medicine, Liverpool, United Kingdom

Abstract

Background:Monitoring malaria transmission is a critical component of efforts to achieve targets for elimination and eradication. Two commonly monitored metrics of transmission intensity are parasite prevalence (PR) and the entomological inoculation rate (EIR). Comparing the spatial and temporal variations in the PR and EIR of a given geographical region and modelling the relationship between the two metrics may provide a fuller picture of the malaria epidemiology of the region to inform control activities.Methods:Using geostatistical methods, we compare the spatial and temporal patterns of Plasmodium falciparum EIR and PR using data collected over 38 months in a rural area of Malawi. We then quantify the relationship between EIR and PR by using empirical and mechanistic statistical models.Results:Hotspots identified through the EIR and PR partly overlapped during high transmission seasons but not during low transmission seasons. The estimated relationship showed a 1-month delayed effect of EIR on PR such that at lower levels of EIR, increases in EIR are associated with rapid rise in PR, whereas at higher levels of EIR, changes in EIR do not translate into notable changes in PR.Conclusions:Our study emphasises the need for integrated malaria control strategies that combine vector and human host managements monitored by both entomological and parasitaemia indices.Funding:This work was supported by Stichting Dioraphte grant number 13050800.

Funder

Stichting Dioraphte

National Institutes of Health

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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