Assessing the role of spatial heterogeneity and human movement in malaria dynamics and control

Prosper, Olivia; Ruktanonchai, Nick W.; Martcheva, Maia

doi:10.1016/j.jtbi.2012.02.010

Cited by 56 publications

(44 citation statements)

References 48 publications

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“…There are numerous transmission models in mosquito-borne diseases, which frequently use parameter estimation in fitting these models to data, and broader issues of parameter uncertainty and sensitivity have often been raised and explored (Johnson et al, 2015; Manore et al, 2014; Prosper et al, 2012; Reich et al, 2013; Mendes Luz et al, 2003; Laneri et al, 2010; Pandey et al, 2013; Shutt et al, 2017). However, relatively few efforts have been made to formally examine questions of parameter identifiability in these models (Mendes Luz et al, 2003; Laneri et al, 2010; Bhadra et al, 2011; Moulay et al, 2012b; Pandey et al, 2013; Reiner et al, 2014; Zhu et al, 2015; Tuncer et al, 2016; Shutt et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Practical unidentifiability of a simple vector-borne disease model: Implications for parameter estimation and intervention assessment

Kao

Eisenberg

2018

Epidemics

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Mathematical modeling has an extensive history in vector-borne disease epidemiology, and is increasingly used for prediction, intervention design, and understanding mechanisms. Many studies rely on parameter estimation to link models and data, and to tailor predictions and counterfactuals to specific settings. However, few studies have formally evaluated whether vector-borne disease models can properly estimate the parameters of interest given the constraints of a particular dataset. Identifiability analysis allows us to examine whether model parameters can be estimated uniquely—a lack of consideration of such issues can result in misleading or incorrect parameter estimates and model predictions. Here, we evaluate both structural (theoretical) and practical identifiability of a commonly used compartmental model of mosquito-borne disease, using the 2010 dengue epidemic in Taiwan as a case study. We show that while the model is structurally identifiable, it is practically unidentifiable under a range of human and mosquito time series measurement scenarios. In particular, the transmission parameters form a practically identifiable combination and thus cannot be estimated separately, potentially leading to incorrect predictions of the effects of interventions. However, in spite of the unidentifiability of the individual parameters, the basic reproduction number was successfully estimated across the unidentifiable parameter ranges. These identifiability issues can be resolved by directly measuring several additional human and mosquito life-cycle parameters both experimentally and in the field. While we only consider the simplest case for the model, we show that a commonly used model of vector-borne disease is unidentifiable from human and mosquito incidence data, making it diﬃcult or impossible to estimate parameters or assess intervention strategies. This work illustrates the importance of examining identifiability when linking models with data to make predictions and inferences, and particularly highlights the importance of combining laboratory, field, and case data if we are to successfully estimate epidemiological and ecological parameters using models.

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Section: Introductionmentioning

confidence: 99%

Practical unidentifiability of a simple vector-borne disease model: Implications for parameter estimation and intervention assessment

Kao

Eisenberg

2018

Epidemics

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“…Being able to predict how control will affect parasite populations across networks of patches has been characterized statistically, but it also requires a mechanistic understanding of transmission and parasite mobility, as mediated by both mosquitoes and humans. However, most algorithms for quantifying transmission intensity across heterogeneous landscape either do not incorporate mobility in both hosts [29,38] or are purely statistical identification methods [8]. In the multi-patch Ross-Macdonald model we analyzed to identify patches that are transmission foci, which incorporates both human and mosquito movement, we test whether targeting foci based on local estimates of transmission is a viable strategy for eliminating parasite populations regionally.…”

Section: Resultsmentioning

confidence: 99%

Parasite sources and sinks in a patched Ross–Macdonald malaria model with human and mosquito movement: Implications for control

Ruktanonchai

Smith

Leenheer

2016

Mathematical Biosciences

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We consider the dynamics of a mosquito-transmitted pathogen in a multipatch Ross-Macdonald malaria model with mobile human hosts, mobile vectors, and a heterogeneous environment. We show the existence of a globally stable steady state, and a threshold that determines whether a pathogen is either absent from all patches, or endemic and present at some level in all patches. Each patch is characterized by a local basic reproduction number, whose value predicts whether the disease is cleared or not when the patch is isolated: patches are known as "demographic sinks" if they have a local basic reproduction number less than one, and hence would clear the disease if isolated; patches with a basic reproduction number above one would sustain endemic infection in isolation, and become "demographic sources" of parasites when connected to other patches. Sources are also considered focal areas of transmission for the larger landscape, as they export excess parasites to other areas and can sustain parasite populations. We show how to determine the various basic reproduction numbers from steady state estimates in the patched network and knowledge of additional model parameters, hereby identifying parasite sources in the process. This is useful in the context of control of the infection on natural landscapes, because a commonly suggested strategy is to target focal areas, in order to make their corresponding basic reproduction numbers less than one, effectively turning them into sinks. We show that this is indeed a successful control strategy -albeit a conservative and possibly expensive one-in case either the human host, or the vector does not move. However, we also show that when both humans and vectors move, this strategy may fail, depending on the specific movement patterns exhibited by hosts and vectors.

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“…The outputs of SDMs driven by LCM projections may be connected further to mathematical models such as metapopulation models that consider patch size and connectivity to simulate colonization, extinction, as well as pathogen transmission and epidemic processes [44]. In addition, future applications of LCMs may include growth of linear connections, particularly establishment of new roads and irrigation canals that are likely to occur in the near future.…”

Section: Discussionmentioning

confidence: 99%

“…This analysis served to provide basic information on the spatial configuration of projected cropland, including gaps, patch size, corridor length, etc., from which one may infer re-invasion potential through the Nile Valley under different scenarios. Such data may also be useful in guiding parameterization of new metapopulation models that consider habitat patch dynamics of malaria vectors in areas of low transmission [44]. …”

Section: Methodsmentioning

confidence: 99%

Linking land cover and species distribution models to project potential ranges of malaria vectors: an example using Anopheles arabiensis in Sudan and Upper Egypt

Fuller

Parenti²,

Hassan

et al. 2012

Malar J

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BackgroundAnopheles arabiensis is a particularly opportunistic feeder and efficient vector of Plasmodium falciparum in Africa and may invade areas outside its normal range, including areas separated by expanses of barren desert. The purpose of this paper is to demonstrate how spatial models can project future irrigated cropland and potential, new suitable habitat for vectors such as An. arabiensis.MethodsTwo different but complementary spatial models were linked to demonstrate their synergy for assessing re-invasion potential of An. arabiensis into Upper Egypt as a function of irrigated cropland expansion by 2050. The first model (The Land Change Modeler) was used to simulate changes in irrigated cropland using a Markov Chain approach, while the second model (MaxEnt) uses species occurrence points, land cover and other environmental layers to project probability of species presence. Two basic change scenarios were analysed, one involving a more conservative business-as-usual (BAU) assumption and second with a high probability of desert-to-cropland transition (Green Nile) to assess a broad range of potential outcomes by 2050.ResultsThe results reveal a difference of 82,000 sq km in potential An. arabiensis range between the BAU and Green Nile scenarios. The BAU scenario revealed a highly fragmented set of small, potential habitat patches separated by relatively large distances (maximum distance = 64.02 km, mean = 12.72 km, SD = 9.92), while the Green Nile scenario produced a landscape characterized by large patches separated by relatively shorter gaps (maximum distance = 49.38, km, mean = 4.51 km, SD = 7.89) that may be bridged by the vector.ConclusionsThis study provides a first demonstration of how land change and species distribution models may be linked to project potential changes in vector habitat distribution and invasion potential. While gaps between potential habitat patches remained large in the Green Nile scenario, the models reveal large areas of future habitat connectivity that may facilitate the re-invasion of An. arabiensis from Sudan into Upper Egypt. The methods used are broadly applicable to other land cover changes as they influence vector distribution, particularly those related to tropical deforestation and urbanization processes.

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Assessing the role of spatial heterogeneity and human movement in malaria dynamics and control

Cited by 56 publications

References 48 publications

Practical unidentifiability of a simple vector-borne disease model: Implications for parameter estimation and intervention assessment

Practical unidentifiability of a simple vector-borne disease model: Implications for parameter estimation and intervention assessment

Parasite sources and sinks in a patched Ross–Macdonald malaria model with human and mosquito movement: Implications for control

Linking land cover and species distribution models to project potential ranges of malaria vectors: an example using Anopheles arabiensis in Sudan and Upper Egypt

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