Mapping malaria by sharing spatial information between incidence and prevalence datasets

Lucas, Tim; Nandi, A.; Chestnutt, Elisabeth G.; Twohig, Katherine A.; Keddie, Suzanne; Collins, Emma; Howes, Rosalind E.; Nguyen, Michele; Rumisha, Susan F.; Python, André; Arambepola, Rohan; Bertozzi‐Villa, Amelia; Hancock, Penelope A.; Amratia, Punam; Battle, Katherine E.; Cameron, Ewan; Gething, Peter W.; Weiss, Daniel J.

doi:10.1101/2020.02.14.20023069

Cited by 4 publications

(5 citation statements)

References 42 publications

(66 reference statements)

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“…There are examples in which area level data are combined with data collected at specific point locations (for example from prevalence surveys) within a joint model that draws on the information of both spatial scales [16,21,19,44]. Wilson and Wakefield [44] found deterioration of accuracy when fitting only to areal census data versus point and areal, which worsened when cases were split across larger areas.…”

Section: Discussionmentioning

confidence: 99%

“…There may be scope for developing the current implementation of disaggregation regression to employ restricted spatial regression as suggested in [45], fitting the spatial random effects only within the residual space after adjustment for the specified fixed effects. Lucas et al [16] demonstrated the use of machine learning techniques to first identify relevant non-linear relationships with covariates from point-prevalence data to then feed into a disaggregation model, and found that this improved accuracy relative to a baseline using only the raw covariates. -5800] and 1400 [600 -3080], respectively).…”

Section: Limitationsmentioning

confidence: 99%

“…The analysis presented here aims to draw inference from routinely reported VL diagnoses at the block level, combining this with remotely-sensed covariate data and exploiting a disaggregation approach [13] to infer the potential distribution of those cases at a more local level. Also sometimes referred to as "downscaling", methods for inferring fine scale variation from spatially aggregated data have progressed substantially in recent years, alongside computational developments in the field of spatial statistics more broadly [14,15,16,17,18,19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

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Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales

Nightingale,

Subramanian,

Schwarzer

et al. 2024

Preprint

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As cases of visceral leishmaniasis (VL) dwindle, there is justification to pursue a finer scale than the sub-district ("block") for monitoring the progress of elimination in India. Low incidence projections across broad regions of unevenly-distributed communities are difficult to act upon, and focal pockets of incidence cannot be overlooked if the goal is to avoid resurgence and sustain equitable elimination. However, maintaining consistent surveillance at a fine scale is difficult to justify in a low-resource elimination setting and not sustainable in the long-term. This work analyses village-level incidence across the whole of Bihar. Spatial auto-correlation in observed incidence and associations with local environmental conditions are explored, and a statistical disaggregation approach is evaluated to infer village-level variation from routine block-level data. We found that the approach did not estimate village-level incidence more accurately than a baseline assumption of block-homogenous incidence, and that this inaccuracy did not appear to be due to non-linear or interacting covariate effects. We suggest that mechanisms of the current surveillance system may limit the predictability of observed village-level incidence, and that this should be a consideration when interpreting the output of disaggregation models. Spatial autocorrelation is evident on a block-level but appears weak between neighbouring villages within individual blocks, suggesting that an important transmission mechanism may act at a longer range (for example due to population movement). Increasing the range of reactive interventions to neighbouring villages may therefore not improve efficacy, however increased routine surveillance and treatment among mobile population groups may be beneficial in reducing the risk of reintroduction into previously unaffected villages.

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales

Nightingale,

Subramanian,

Schwarzer

et al. 2024

Preprint

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“…Providing fine‐scale risk maps where there is insufficient data to inform these predictions (eg, where response data is aggregated over very large areas or information is only available from a small number of areas) may be misleading and in these cases aggregated risk maps may be more suitable. Cross validation can be done on the aggregate level (eg, as by Lucas et al, 12 Law et al 13 ) but again it is not clear how measures of out‐of‐sample aggregated performance relate to fine‐scale predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

A simulation study of disaggregation regression for spatial disease mapping

Arambepola

Lucas

Nandi

et al. 2021

Statistics in Medicine

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Disaggregation regression has become an important tool in spatial disease mapping for making fine‐scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modeling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine‐scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine‐scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well‐specified, fine‐scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross‐validation correlation on the aggregate level was a moderately good predictor of fine‐scale predictive performance. While these simulations are unlikely to capture the nuances of real‐life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.

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“…Battle and others (2019);Arambepola and others (2020)). Many of the environmental factors affect mosquito breeding habitats (typically pools of stagnant water) or parasite development, while the socioeconomic variables may be correlated with access to healthcare or rural/urban settings.…”

mentioning

confidence: 97%

A simulation study of disaggregation regression for spatial disease mapping

Arambepola¹,

Lucas²,

Nandi³

et al. 2020

Preprint

Self Cite

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Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modelling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the * To whom correspondence should be addressed.

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Mapping malaria by sharing spatial information between incidence and prevalence datasets

Cited by 4 publications

References 42 publications

Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales

Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales

A simulation study of disaggregation regression for spatial disease mapping

A simulation study of disaggregation regression for spatial disease mapping

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