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Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting.

ABSTRACT: In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, R_c, describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55-0.65), individual reproduction numbers often exceeded one. We estimate a decline in R_c between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020.

SUBMITTER: Routledge I

PROVIDER: S-EPMC6018772 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting.

Routledge Isobel I Chevéz José Eduardo Romero JER Cucunubá Zulma M ZM Rodriguez Manuel Gomez MG Guinovart Caterina C Gustafson Kyle B KB Schneider Kammerle K Walker Patrick G T PGT Ghani Azra C AC Bhatt Samir S

Nature communications 20180626 1

In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, R<sub>c</sub>, describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typica ...[more]

PMID: 29946060

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Project description:Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer transmission dynamics, in conjunction with additional information such as symptom onset time and genetic distance. However, in the absence of data about the flow of parasites between populations, the spatial scale of malaria transmission is often not clear. As a result, it is important to understand the impact of varying assumptions about the spatial scale of transmission on key metrics of malaria transmission, such as reproduction numbers. We developed a method which allows the flexible integration of distance metrics (such as Euclidian distance, genetic distance or accessibility matrices) with temporal information into a single inference framework to infer malaria reproduction numbers. Twelve scenarios were defined, representing different assumptions about the likelihood of transmission occurring over different geographic distances and likelihood of missing infections (as well as high and low amounts of uncertainty in this estimate). These scenarios were applied to four individual level datasets from malaria eliminating contexts to estimate individual reproduction numbers and how they varied over space and time. Model comparison suggested that including spatial information improved models as measured by second order AIC (ΔAICc), compared to time only results. Across scenarios and across datasets, including spatial information tended to increase the seasonality of temporal patterns in reproduction numbers and reduced noise in the temporal distribution of reproduction numbers. The best performing parameterisations assumed long-range transmission (> 200 km) was possible. Our approach is flexible and provides the potential to incorporate other sources of information which can be converted into distance or adjacency matrices such as travel times or molecular markers.

Project description:BACKGROUND:In Vietnam, malaria persists in remote forested regions where infections are spatially heterogeneous, mostly asymptomatic and with low parasite density. Previous studies in Vietnam have investigated broad behavioural concepts such as 'engaging in forest activities' as risk factors for malaria infection, which may not explain heterogeneity in malaria risk, especially in malaria elimination settings. METHODS:A mixed methods study combining ethnographic research and a cross-sectional survey was embedded in a 1-year malariometric cohort study in three ethnic minority villages in South Tra My district, Quang Nam Province in Central Vietnam. Qualitative data collection included in-depth interviews, informal conversations and participant observations over a 2-month period, and the findings were used to develop the questionnaire used in the cross-sectional survey. The latter collected data on evening activities, mobility patterns and household characteristics. The primary outcome, recent exposure to malaria, was defined using the classification and regression tree method to determine significant changes in antibody titres during the year preceding the survey. Risk factor analyses for recent exposure to malaria were conducted using logistic regression. RESULTS:22 in-depth interviews and numerous participant observations were recorded during the ethnographic research (April to June 2015), and 160 adults (86% response rate) responded to the cross-sectional survey (November to December 2015). Recent exposure to Plasmodium falciparum malaria was estimated at 22.9 and at 17.1% for Plasmodium vivax. Ongoing malaria transmission appears to be maintained by activities that delay or disrupt sleeping in a permanent structure in which a bed net could be hung, including evening drinking gatherings, fishing, logging in the forest and outdoor TV watching. CONCLUSIONS:Vector control tools for outdoor evening activities in villages as well as at farms, forest and river locations should be incorporated into current malaria elimination efforts in Central Vietnam. Micro-epidemiology studies using mixed-methods designs can provide a comprehensive understanding of the malaria risk at fine spatial scales and better inform the implementation of targeted interventions for malaria elimination.

Dataset Information

Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting.

Publications

Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting.

Similar Datasets

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