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      Modelling the impact of interventions on imported, introduced and indigenous malaria infections in Zanzibar, Tanzania

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          Abstract

          Malaria cases can be classified as imported, introduced or indigenous cases. The World Health Organization’s definition of malaria elimination requires an area to demonstrate that no new indigenous cases have occurred in the last three years. Here, we present a stochastic metapopulation model of malaria transmission that distinguishes between imported, introduced and indigenous cases, and can be used to test the impact of new interventions in a setting with low transmission and ongoing case importation. We use human movement and malaria prevalence data from Zanzibar, Tanzania, to parameterise the model. We test increasing the coverage of interventions such as reactive case detection; implementing new interventions including reactive drug administration and treatment of infected travellers; and consider the potential impact of a reduction in transmission on Zanzibar and mainland Tanzania. We find that the majority of new cases on both major islands of Zanzibar are indigenous cases, despite high case importation rates. Combinations of interventions that increase the number of infections treated through reactive case detection or reactive drug administration can lead to substantial decreases in malaria incidence, but for elimination within the next 40 years, transmission reduction in both Zanzibar and mainland Tanzania is necessary.

          Abstract

          Malaria elimination is defined by WHO as the absence of recent indigenous cases in an area. In this study, the authors develop a metapopulation model that identifies indigenous cases and use it to investigate the likelihood of malaria elimination in Zanzibar under different intervention scenarios.

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          Quantifying the impact of human mobility on malaria.

          Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies importation routes that contribute to malaria epidemiology on regional spatial scales.
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            The effects of human movement on the persistence of vector-borne diseases.

            With the recent resurgence of vector-borne diseases due to urbanization and development there is an urgent need to understand the dynamics of vector-borne diseases in rapidly changing urban environments. For example, many empirical studies have produced the disturbing finding that diseases continue to persist in modern city centers with zero or low rates of transmission. We develop spatial models of vector-borne disease dynamics on a network of patches to examine how the movement of humans in heterogeneous environments affects transmission. We show that the movement of humans between patches is sufficient to maintain disease persistence in patches with zero transmission. We construct two classes of models using different approaches: (i) Lagrangian models that mimic human commuting behavior and (ii) Eulerian models that mimic human migration. We determine the basic reproduction number R(0) for both modeling approaches. We show that for both approaches that if the disease-free equilibrium is stable (R(0) 1) then there exists a unique positive (endemic) equilibrium that is globally stable among positive solutions. Finally, we prove in general that Lagrangian and Eulerian modeling approaches are not equivalent. The modeling approaches presented provide a framework to explore spatial vector-borne disease dynamics and control in heterogeneous environments. As an example, we consider two patches in which the disease dies out in both patches when there is no movement between them. Numerical simulations demonstrate that the disease becomes endemic in both patches when humans move between the two patches.
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              Re-imagining malaria: heterogeneity of human and mosquito behaviour in relation to residual malaria transmission in Cambodia

              Background In certain regions in Southeast Asia, where malaria is reduced to forested regions populated by ethnic minorities dependent on slash-and-burn agriculture, malaria vector populations have developed a propensity to feed early and outdoors, limiting the effectiveness of long-lasting insecticide-treated nets (LLIN) and indoor residual spraying (IRS). The interplay between heterogeneous human, as well as mosquito behaviour, radically challenges malaria control in such residual transmission contexts. This study examines human behavioural patterns in relation to the vector behaviour. Methods The anthropological research used a sequential mixed-methods study design in which quantitative survey research methods were used to complement findings from qualitative ethnographic research. The qualitative research existed of in-depth interviews and participant observation. For the entomological research, indoor and outdoor human landing collections were performed. All research was conducted in selected villages in Ratanakiri province, Cambodia. Results Variability in human behaviour resulted in variable exposure to outdoor and early biting vectors: (i) indigenous people were found to commute between farms in the forest, where malaria exposure is higher, and village homes; (ii) the indoor/outdoor biting distinction was less clear in forest housing often completely or partly open to the outside; (iii) reported sleeping times varied according to the context of economic activities, impacting on the proportion of infections that could be accounted for by early or nighttime biting; (iv) protection by LLINs may not be as high as self-reported survey data indicate, as observations showed around 40% (non-treated) market net use while (v) unprotected evening resting and deep forest activities impacted further on the suboptimal use of LLINs. Conclusions The heterogeneity of human behaviour and the variation of vector densities and biting behaviours may lead to a considerable proportion of exposure occurring during times that people are assumed to be protected by the distributed LLINs. Additional efforts in improving LLIN use during times when people are resting in the evening and during the night might still have an impact on further reducing malaria transmission in Cambodia.
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                Author and article information

                Contributors
                aatreyee.das@swisstph.ch
                nakul.chitnis@swisstph.ch
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 May 2023
                12 May 2023
                2023
                : 14
                : 2750
                Affiliations
                [1 ]GRID grid.416786.a, ISNI 0000 0004 0587 0574, Swiss Tropical and Public Health Institute, ; Allschwil, Switzerland
                [2 ]GRID grid.6612.3, ISNI 0000 0004 1937 0642, University of Basel, ; Basel, Switzerland
                [3 ]GRID grid.265219.b, ISNI 0000 0001 2217 8588, Center for Applied Malaria Research and Evaluation, Department of Tropical Medicine, , Tulane University School of Public Health and Tropical Medicine, ; New Orleans, LA USA
                [4 ]GRID grid.414543.3, ISNI 0000 0000 9144 642X, Ifakara Health Institute, ; Dar es Salaam, United Republic of Tanzania
                [5 ]Zanzibar Malaria Elimination Programme, Zanzibar, United Republic of Tanzania
                [6 ]GRID grid.450091.9, ISNI 0000 0004 4655 0462, Present Address: Amsterdam Institute for Global Health and Development Amsterdam, ; Amsterdam, Netherlands
                [7 ]GRID grid.509540.d, ISNI 0000 0004 6880 3010, Present Address: Amsterdam University Medical Centers, ; Amsterdam, Netherlands
                [8 ]Present Address: Office of the Chief Government Statistician (OCGS), Zanzibar, United Republic of Tanzania
                Author information
                http://orcid.org/0000-0001-8849-7776
                http://orcid.org/0000-0002-5285-3254
                http://orcid.org/0000-0002-6160-5295
                http://orcid.org/0000-0002-6825-3472
                http://orcid.org/0000-0002-1103-9141
                Article
                38379
                10.1038/s41467-023-38379-8
                10182017
                37173317
                82c6682c-3fcc-437b-96bb-66fefce564c4
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 September 2022
                : 26 April 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000865, Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation);
                Award ID: INV025569
                Award ID: INV025569
                Award Recipient :
                Funded by: Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation)
                Categories
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                © Springer Nature Limited 2023

                Uncategorized
                malaria,computational models,epidemiology
                Uncategorized
                malaria, computational models, epidemiology

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