Malaria temporal dynamic clustering for surveillance and intervention planning

Legendre, Eva; Lehot, Laurent; Dieng, Sokhna; Thu, Aung Myint; Rae, Jade D.; Delmas, Gilles; Girond, Florian; Herbreteau, Vincent; Nosten, F; Landier, Jordi; Gaudart, Jean

doi:10.1016/j.epidem.2023.100682

Cited by 2 publications

(12 citation statements)

References 16 publications

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“…This study was based on previous work that described malaria incidence heterogeneity in Karen/Kayin State from March 2016 to February 2020 for 662 villages [ 14 ]. Based on P. falciparum and P. vivax weekly incidence reported by MPs, villages sharing similar dynamics were grouped in P. falciparum and P. vivax ‘incidence profiles’.…”

Section: Methodsmentioning

confidence: 99%

“…Incidence profiles were determined by partitioning around medoids clustering combined with dynamic time warping distance following functional time series smoothing. The methodology is described in this earlier study [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

“…Eleven P. falciparum and 11 P. vivax incidence profiles were identified in this previous work [ 14 ] and are used as the outcomes in the present study. Plasmodium falciparum incidence profiles distinguished villages by the amplitude of the incidence (Very low, Low vs. Hotspot) and the occurrence of seasonal sporadic annual peaks (Rainy 2017, Rainy 2018, Cold 2018–2019, etc.).…”

Section: Methodsmentioning

confidence: 99%

“…Grey lines represent functional incidence rate of all villages included in the cluster across study period. This original figure presents data and results published previously [ 14 ] …”

Section: Methodsmentioning

confidence: 99%

“…Malaria-affected incidence profiles ( n = 123 for P. falciparum and n = 205 for P. vivax ) were grouped in the Northern Township of Karen/Kayin State, and along the Thailand-Myanmar border for P. vivax (Additional file 1 : Fig. S1) [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

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‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments

Legendre,

Girond,

Herbreteau

et al. 2023

Parasites Vectors

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Background In the Greater Mekong Subregion, case–control studies and national-level analyses have shown an association between malaria transmission and forest activities. The term ‘forest malaria’ hides the diversity of ecosystems in the GMS, which likely do not share a uniform malaria risk. To reach malaria elimination goals, it is crucial to document accurately (both spatially and temporally) the influence of environmental factors on malaria to improve resource allocation and policy planning within given areas. The aim of this ecological study is to characterize the association between malaria dynamics and detailed ecological environments determined at village level over a period of several years in Kayin State, Myanmar. Methods We characterized malaria incidence profiles at village scale based on intra- and inter-annual variations in amplitude, seasonality, and trend over 4 years (2016–2020). Environment was described independently of village localization by overlaying a 2-km hexagonal grid over the region. Specifically, hierarchical classification on principal components, using remote sensing data of high spatial resolution, was used to assign a landscape and a climate type to each grid cell. We used conditional inference trees and random forests to study the association between the malaria incidence profile of each village, climate and landscape. Finally, we constructed eco-epidemiological zones to stratify and map malaria risk in the region by summarizing incidence and environment association information. Results We identified a high diversity of landscapes (n = 19) corresponding to a gradient from pristine to highly anthropogenically modified landscapes. Within this diversity of landscapes, only three were associated with malaria-affected profiles. These landscapes were composed of a mosaic of dense and sparse forest fragmented by small agricultural patches. A single climate with moderate rainfall and a temperature range suitable for mosquito presence was also associated with malaria-affected profiles. Based on these environmental associations, we identified three eco-epidemiological zones marked by later persistence of Plasmodium falciparum, high Plasmodium vivax incidence after 2018, or a seasonality pattern in the rainy season. Conclusions The term forest malaria covers a multitude of contexts of malaria persistence, dynamics and populations at risk. Intervention planning and surveillance could benefit from consideration of the diversity of landscapes to focus on those specifically associated with malaria transmission. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Grey lines represent functional incidence rate of all villages included in the cluster across study period. This original figure presents data and results published previously [ 14 ] …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments

Legendre,

Girond,

Herbreteau

et al. 2023

Parasites Vectors

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Geo-epidemiology of malaria incidence in the Vhembe District to guide targeted elimination strategies, South-Africa, 2015–2018: a local resurgence

Dieng

Adebayo-Ojo

Kruger

et al. 2023

Sci Rep

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In South Africa, the population at risk of malaria is 10% (around six million inhabitants) and concern only three provinces of which Limpopo Province is the most affected, particularly in Vhembe District. As the elimination approaches, a finer scale analysis is needed to accelerate the results. Therefore, in the process of refining local malaria control and elimination strategies, the aim of this study was to identify and describe malaria incidence patterns at the locality scale in the Vhembe District, Limpopo Province, South Africa. The study area comprised 474 localities in Vhembe District for which smoothed malaria incidence curve were fitted with functional data method based on their weekly observed malaria incidence from July 2015 to June 2018. Then, hierarchical clustering algorithm was carried out considering different distances to classify the 474 smoothed malaria incidence curves. Thereafter, validity indices were used to determine the number of malaria incidence patterns. The cumulative malaria incidence of the study area was 4.1 cases/1000 person-years. Four distinct patterns of malaria incidence were identified: high, intermediate, low and very low with varying characteristics. Malaria incidence increased across transmission seasons and patterns. The localities in the two highest incidence patterns were mainly located around farms, and along the rivers. Some unusual malaria phenomena in Vhembe District were also highlighted as resurgence. Four distinct malaria incidence patterns were found in Vhembe District with varying characteristics. Findings show also unusual malaria phenomena in Vhembe District that hinder malaria elimination in South Africa. Assessing the factors associated with these unusual malaria phenome would be helpful on building innovative strategies that lead South Africa on malaria elimination.

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Malaria temporal dynamic clustering for surveillance and intervention planning

Cited by 2 publications

References 16 publications

‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments

‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments

Geo-epidemiology of malaria incidence in the Vhembe District to guide targeted elimination strategies, South-Africa, 2015–2018: a local resurgence

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