Merging Economics and Epidemiology to Improve the Prediction and Management of Infectious Disease

Perrings, Charles; Castillo‐Chavez, Carlos; Chowell, Gerardo; Daszak, Peter; Fenichel, Eli P.; Finnoff, David; Horan, Richard D.; Kilpatrick, A. Marm; Kinzig, Ann P.; Kuminoff, Nicolai V.; Levin, Simon A.; Morin, B.; Smith, Katherine F.; Springborn, Michael

doi:10.1007/s10393-014-0963-6

Cited by 105 publications

(101 citation statements)

References 121 publications

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“…Recent studies [31, 42, 43, 44, 61] have incorporated behavior as a feedback response coupled with the dynamics of the disease. A model of the decision to spend time in patch i = 1, 2 based on individuals’ utility functions that include the possibility of adapting to changing contagion dynamics in the above two patch setting, using previous work [31, 58], is the subject of a separate study.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

SIS and SIR Epidemic Models Under Virtual Dispersal

et al. 2015

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We develop a multi-group epidemic framework via virtual dispersal where the risk of infection is a function of the residence time and local environmental risk. This novel approach eliminates the need to define and measure contact rates that are used in the traditional multi-group epidemic models with heterogeneous mixing. We apply this approach to a general n-patch SIS model whose basic reproduction number R0 is computed as a function of a patch residence-times matrix ℙ. Our analysis implies that the resulting n-patch SIS model has robust dynamics when patches are strongly connected: there is a unique globally stable endemic equilibrium when R0 > 1 while the disease free equilibrium is globally stable when R0 ≤ 1. Our further analysis indicates that the dispersal behavior described by the residence-times matrix ℙ has profound effects on the disease dynamics at the single patch level with consequences that proper dispersal behavior along with the local environmental risk can either promote or eliminate the endemic in particular patches. Our work highlights the impact of residence times matrix if the patches are not strongly connected. Our framework can be generalized in other endemic and disease outbreak models. As an illustration, we apply our framework to a two-patch SIR single outbreak epidemic model where the process of disease invasion is connected to the final epidemic size relationship. We also explore the impact of disease prevalence driven decision using a phenomenological modeling approach in order to contrast the role of constant versus state dependent ℙ on disease dynamics.

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Section: Conclusion and Discussionmentioning

confidence: 99%

SIS and SIR Epidemic Models Under Virtual Dispersal

et al. 2015

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“…The relevance of economic factors for epidemiology is receiving increased attention in the emerging field of epidemiological economics (Perrings et al 2014). Epidemiological economics, or EE, focuses on the ways in which economic factors influence human decisions about how and with whom they interact.…”

Section: Macro-scale Influencesmentioning

confidence: 99%

A social–ecological approach to landscape epidemiology: geographic variation and avian influenza

et al. 2015

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Context Landscape structure influences host-parasite-pathogen dynamics at multiple scales in space and time. Landscape epidemiology, which connects disease ecol-ogy and landscape ecology, is still an emerging field. Objective We argue that landscape epidemiology must move beyond simply studying the influence of landscape configuration and composition on epidemiological processes and towards a more comparative, systems approach that better incorporates social-ecological complexity. Methods We illustrate our argument with a detailed review, based on a single conceptual systems model, of geographic variation in drivers of avian influenza in Western Europe, Southeast Asia, and Southern Africa. Results Our three study regions are similar in some ways but quite different in others. The same underlying mechanisms apply in all cases, but differences in the attributes of key components and linkages (most notably avian diversity, the abiotic environment, land use and land cover, and food production systems) create significant differences in avian influenza virus prevalence and human risk between regions. Conclusions Landscape approaches can connect local-and continental-scale elements of epidemiology. Adopting a landscape-focused systems per-spective on the problem facilitates the identification of the most important commonalities and differences, guiding both science and policy, and helps to identify elements of the problem on which further research is needed. More generally, our review demonstrates the importance of socialecological interactions and comparative approaches for landscape epidemiology.

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“…The authors demonstrate that fitting the classical SIR model to data generated by their new framework results in erroneous estimates of epidemiological parameters, because of its inability to jointly estimate behavioral and biological parameters. See Perrings et al for a thorough review on the growing topic of economic epidemiology [65]. …”

Section: Resultsmentioning

confidence: 99%

Challenges in modeling complexity of neglected tropical diseases: a review of dynamics of visceral leishmaniasis in resource limited settings

DebRoy

Prosper

Mishoe

et al. 2017

Emerg Themes Epidemiol

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ObjectivesNeglected tropical diseases (NTD), account for a large proportion of the global disease burden, and their control faces several challenges including diminishing human and financial resources for those distressed from such diseases. Visceral leishmaniasis (VL), the second-largest parasitic killer (after malaria) and an NTD affects poor populations and causes considerable cost to the affected individuals. Mathematical models can serve as a critical and cost-effective tool for understanding VL dynamics, however, complex array of socio-economic factors affecting its dynamics need to be identified and appropriately incorporated within a dynamical modeling framework. This study reviews literature on vector-borne diseases and collects challenges and successes related to the modeling of transmission dynamics of VL. Possible ways of creating a comprehensive mathematical model is also discussed.MethodsPublished literature in three categories are reviewed: (i) identifying non-traditional but critical mechanisms for VL transmission in resource limited regions, (ii) mathematical models used for dynamics of Leishmaniasis and other related vector borne infectious diseases and (iii) examples of modeling that have the potential to capture identified mechanisms of VL to study its dynamics.ResultsThis review suggests that VL elimination have not been achieved yet because existing transmission dynamics models for VL fails to capture relevant local socio-economic risk factors. This study identifies critical risk factors of VL and distribute them in six categories (atmosphere, access, availability, awareness, adherence, and accedence). The study also suggests novel quantitative models, parts of it are borrowed from other non-neglected diseases, for incorporating these factors and using them to understand VL dynamics and evaluating control programs for achieving VL elimination in a resource-limited environment.ConclusionsControlling VL is expensive for local communities in endemic countries where individuals remain in the vicious cycle of disease and poverty. Smarter public investment in control programs would not only decrease the VL disease burden but will also help to alleviate poverty. However, dynamical models are necessary to evaluate intervention strategies to formulate a cost-effective optimal policy for eradication of VL.Electronic supplementary materialThe online version of this article (doi:10.1186/s12982-017-0065-3) contains supplementary material, which is available to authorized users.

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Merging Economics and Epidemiology to Improve the Prediction and Management of Infectious Disease

Cited by 105 publications

References 121 publications

SIS and SIR Epidemic Models Under Virtual Dispersal

SIS and SIR Epidemic Models Under Virtual Dispersal

A social–ecological approach to landscape epidemiology: geographic variation and avian influenza

Challenges in modeling complexity of neglected tropical diseases: a review of dynamics of visceral leishmaniasis in resource limited settings

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