1
|
Sedda L, McCann RS, Kabaghe AN, Gowelo S, Mburu MM, Tizifa TA, Chipeta MG, van den Berg H, Takken W, van Vugt M, Phiri KS, Cain R, Tangena JAA, Jones CM. Hotspots and super-spreaders: Modelling fine-scale malaria parasite transmission using mosquito flight behaviour. PLoS Pathog 2022; 18:e1010622. [PMID: 35793345 PMCID: PMC9292116 DOI: 10.1371/journal.ppat.1010622] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 07/18/2022] [Accepted: 05/27/2022] [Indexed: 11/19/2022] Open
Abstract
Malaria hotspots have been the focus of public health managers for several years due to the potential elimination gains that can be obtained from targeting them. The identification of hotspots must be accompanied by the description of the overall network of stable and unstable hotspots of malaria, especially in medium and low transmission settings where malaria elimination is targeted. Targeting hotspots with malaria control interventions has, so far, not produced expected benefits. In this work we have employed a mechanistic-stochastic algorithm to identify clusters of super-spreader houses and their related stable hotspots by accounting for mosquito flight capabilities and the spatial configuration of malaria infections at the house level. Our results show that the number of super-spreading houses and hotspots is dependent on the spatial configuration of the villages. In addition, super-spreaders are also associated to house characteristics such as livestock and family composition. We found that most of the transmission is associated with winds between 6pm and 10pm although later hours are also important. Mixed mosquito flight (downwind and upwind both with random components) were the most likely movements causing the spread of malaria in two out of the three study areas. Finally, our algorithm (named MALSWOTS) provided an estimate of the speed of malaria infection progression from house to house which was around 200-400 meters per day, a figure coherent with mark-release-recapture studies of Anopheles dispersion. Cross validation using an out-of-sample procedure showed accurate identification of hotspots. Our findings provide a significant contribution towards the identification and development of optimal tools for efficient and effective spatio-temporal targeted malaria interventions over potential hotspot areas.
Collapse
Affiliation(s)
- Luigi Sedda
- Lancaster Ecology and Epidemiology Group, Lancaster Medical School, Lancaster University, United Kingdom
| | - Robert S. McCann
- Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands
- School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Alinune N. Kabaghe
- School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Steven Gowelo
- Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands
- School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi
- MAC Communicable Diseases Action Centre, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Monicah M. Mburu
- Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands
- School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Tinashe A. Tizifa
- School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi
- Center for Tropical Medicine and Travel Medicine, University of Amsterdam, The Netherlands
| | - Michael G. Chipeta
- School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi
| | - Henk van den Berg
- Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands
| | - Willem Takken
- Laboratory of Entomology, Wageningen University & Research, Wageningen, The Netherlands
| | - Michèle van Vugt
- Center for Tropical Medicine and Travel Medicine, University of Amsterdam, The Netherlands
| | - Kamija S. Phiri
- School of Global and Public Health, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Russell Cain
- Lancaster Ecology and Epidemiology Group, Lancaster Medical School, Lancaster University, United Kingdom
| | - Julie-Anne A. Tangena
- Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Christopher M. Jones
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi
- Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| |
Collapse
|
2
|
Assessing intervention strategies for non-homogeneous populations using a closed form formula for R 0. J Theor Biol 2020; 511:110561. [PMID: 33347895 DOI: 10.1016/j.jtbi.2020.110561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 10/29/2020] [Accepted: 12/07/2020] [Indexed: 11/22/2022]
Abstract
A general stochastic model for susceptible → infective → recovered (SIR) epidemics in non-homogeneous populations is considered. The heterogeneity is a very important aspect here since it allows more realistic but also more complex models. The basic reproduction number R0, an indication of the probability of an outbreak for homogeneous populations does not indicate the probability of an outbreak for non-homogeneous models anymore, because it changes with the initially infected case. Therefore, we use "individual R0" that is the expected number of secondary cases for a unique given initially infected individual. Thus, the effectiveness of intervention strategies can be assessed by their capability to reduce individual R0 values. Also a vaccination plan based on individual R0 values for fully heterogeneous populations is proposed. It is based on the recursive calculation of individual R0 values.
Collapse
|