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Modeling Anthropogenic Effects in the Spread of Infectious Diseases (MASpread)

Eco Services Group

Selected project publications

Morin, B.R., Perrings, C., Kinzig, A. & Levin, S. (2015) The Social Benefit of Private Infectious Disease-Risk Mitigation. Theoretical Ecology DOI 10.1007/s12080-015-0262-z.

Springborn, M., Chowell, G., MacLachlan, M. & Fenichel, E.P. (2015) Accounting for behavioral responses during a flu epidemic using home television viewing. BMC infectious diseases, 15, 21.

Perrings, C., Castillo-Chavez, C., Chowell, G., Daszak, P., Fenichel, E., Finnoff, D., Horan, R., Kilpatrick, A.M., Kinzig, A., Kuminoff, N., Levin, S., Morin, B., Smith, K. & Springborn, M. (2014) Merging Economics and Epidemiology to Improve the Prediction and Management of Infectious Disease. EcoHealth, 11, 464-475.

Morin, B.R., Perrings, C., Levin, S. & Kinzig, A. (2014) Disease risk mitigation: The equivalence of two selective mixing strategies on aggregate contact patterns and resulting epidemic spread. Journal of Theoretical Biology, 363, 262-270.

Kang Y, Castillo-Chavez C.  (2014) A simple epidemiological model for populations in the wild with Allee effects and disease-modified fitness.  Discrete Continuous Dyn Syst Ser B, 19(1): 89-130.

Hernandez-Ceron N, Feng Z, Castillo-Chavez C.  (2013) Discrete epidemic models with arbitrary stage distributions and applications to disease control. Bull Math Biol. 75(10): 1716-46.

Fenichel EP.  (2013) Economic considerations for social distancing and behavioral based policies during an epidemic.  J Health Econ. 32(2): 440-51

Rahman SA, Hassan L, Epstein JH, Mamat ZC, Yatim AM, Hassan SS, Field HE, Hughes T, Westrum J, Naim MS, Suri AS, Jamaluddin AA, Daszak P. (2013) Henipavirus Ecology Research Group.  Risk Factors for Nipah virus infection among pteropid bats, Peninsular Malaysia.  Emerg Infect Dis. 19(1): 51-60

Olival KJ, Islam A, Yu M, Anthony SJ, Epstein JH, Khan SA, Khan SU, Crameri G, Wang LF, Lipkin WI, Luby SP, Daszak P.  (2013) Ebola virus antibodies in fruit bats, Bangladesh.  Emerg Infect Dis. 19(2): 270-273.

Sazzad HM, Hossain MJ, Gurley ES, Ameen KM, Parveen S, Islam MS, Faruque LI, Podder G, Banu SS, Lo MK, Rollin PE, Rota PA, Daszak P, Rahman M, Luby SP.  Nipah virus infection outbreak with nosocomial and corpse-to-human transmission, Bangladesh.  (2013) Emerg Infect Dis. 19(2):210-7.

Bogich TL, Funk S, Malcolm TR, Chhun N, Epstein JH, Chmura AA, Kilpatrick AM, Brownstein JS, Hutchison OC, Doyle-Capitman C, Deaville R, Morse SS, Cunningham AA, Daszak P. (2013) Using network theory to identify the causes of disease outbreaks of unknown origin.  J R Soc Interface. 10(81): 20120904

Fuller TL, Gilbert M, Martin V, Cappelle J, Hosseini P, Njabo KY, Abdel Aziz S, Xiao X, Daszak P, Smith TB.  (2013) Predicting hotspots for influenza virus reassortment.
Emerg Infect Dis. 2013 Apr; 19(4): 581-8

Murray KA, Daszak P. (2013) Human ecology in pathogenic landscapes: two hypotheses on how land use change drives viral emergence. Curr Opin Virol. 3(1): 79-83

Fenichel, E.P., Richards, T.J. & Shanafelt, D.W. (2013) The Control of Invasive Species on Private Property with Neighbor-to-Neighbor Spillovers. Environmental and Resource Economics, 1-25.
Morin, B.R., Fenichel, E.P. & Castillo-Chavez, C. (2013) SIR dynamics with economically driven contact rates. Natural resource modeling, 26, 505-525.

Fenichel EP, Kuminoff NV, Chowell G. Skip the trip: air travelers' behavioral responses to pandemic influenza. PLoS One. 2013; 8(3) :e58249.

Fenichel EP, Wang X. Modeling the Interplay between Human Behavior and Spread of Infectious Diseases. d'Onofrio A, Manfredi P, editors. NY: Springer; 2013. The mechanism and phenomenon of adaptive human behavior during an epidemic and the role of information; p.153-169.

Chowell G, Nishiura H, Viboud C. Modeling rapidly disseminating infectious disease during mass gatherings. BMC Med. 10: 159.

Morse SS, Mazet JA, Woolhouse M, Parrish CR, Carroll D, Karesh WB, Zambrana-Torrelio C, Lipkin WI, Daszak P. 2012 Prediction and prevention of the next pandemic zoonosis. Lancet; 380(9857): 1956- 65.

Daszak P. (2012) Anatomy of a pandemic. Lancet. 380(9857): 1883-4.

Chowell G, Towers S, Viboud C, Fuentes R, Sotomayor V, Simonsen L, Miller MA, Lima M, Villarroel C, Chiu M, Villarroel JE, Olea A. (2012) The influence of climatic conditions on the transmission dynamics of the 2009 A/H1N1 influenza pandemic in Chile. BMC Infect Dis. 12: 298.

Chowell G, Nishiura H. (2012) Toward unbiased assessment of treatment and prevention: modeling household transmission of pandemic influenza. BMC Med. 10:118.

Shim E, Feng Z, Castillo-Chavez C. Differential impact of sickle cell trait on symptomatic and asymptomatic malaria. (2012) Math Biosci Eng. 9(4): 877-98.

Paull SH, Song S, McClure KM, Sackett LC, Kilpatrick AM, Johnson PT. (2012) From superspreaders to disease hotspots: linking transmission across hosts and space. Front Ecol Environ. 10(2): 75-82.

Smith KM, Anthony SJ, Switzer WM, Epstein JH, Seimon T, Jia H, Sanchez MD, Huynh TT, Galland GG, Shapiro SE, Sleeman JM, McAloose D, Stuchin M, Amato G, Kolokotronis SO, Lipkin WI, Karesh WB, Daszak P, Marano N. (2012) Zoonotic viruses associated with illegally imported wildlife products. PLoS One. 7(1): e29505.

Horan RD, Wolf CA, Fenichel EP. (2012) Health and Animal Agriculture in Developing Countries. Zilberman D, Otte J, Roland-Holst D, Pfeiffer D, editors. Dynamic Perspectives on the Control of Animal Disease: Merging Epidemiology and Economics, Berlin: Springer, p.101-118.

Wang Q, Fenichel EP, Perrings CA. (2012) Health and Animal Agriculture in Developing Countries. Zilberman D, Otte J, Roland-Holst D, Pfeiffer D, editors. Border inspection and trade diversion: risk reduction versus risk substitution, Berlin: Springer, p.119-134.



Modeling Anthropogenic Effects in the Spread of Infectious Diseases (MASpread)

Project description

There is increasing interest in modeling risks associated with emerging infectious diseases (EIDs). Most EIDs are zoonotic in nature, and many infect valuable livestock and wildlife resources. Disease risks, like the risks associated with invasive species, are endogenous – a function of human decisions. However, most current attempts to model EID risks treat risk as exogenous. In order to successfully manage, predict, and develop surveillance strategies for new emerging diseases it is important to model the impact of human decisions on disease risks.

MASpread investigates the economic drivers of ‘contact’ in dynamic models of emerging human and animal infectious disease systems. The project analyzes disease system dynamics with and without adaptive responses. The models will be calibrated for a set of diseases where people's trade and travel decisions are potentially important (e.g. H1N1, H5N1, FMD). The aim is to strengthen the power of compartmental epidemiological models (a) to predict the likelihood that diseases of particular types will be introduced and the course of diseases once introduced, and (b) to evaluate the potential for incentive-based policy responses to disease threats and disease outbreaks.

Project personnel

The MASPread research team has been built over a number of years through collaboration in three networks: an RCN – BESTNet; the international biodiversity science program DIVERSITAS; and a NIMBIOS working group – SPIDER. The research team comprises mathematicians, epidemiologists, ecologists and resource economists. The team comprises:

Arizona State University: C.Perrings (PI), C. Castillo-Chavez, G. Chowell-Puente, N. Kuminoff, A. Kinzig

Princeton University: S. Levin

University of California Davis: M. Springborn

University of California Santa Cruz; M. Kilpatrick

Michigan State University: R. Horan

Ecohealth Alliance: P. Daszak

Brown University: K. Smith

University of Wyoming: D. Finnoff

Studies and results

Theoretical studies

1. Models of adaptive behavior. We developed a mechanistic model that uses microeconomic theory to describe the adaptive or strategic behavior of susceptible or infected individuals. We show that phenomenological forecasting models and forecasting models based on epidemiological theory guide human behavior towards similar biological outcomes, but different levels of social wellbeing. Moreover, we find that assumptions about individual preferences have a substantial influence on epidemics.

2. Feedback models. We developed a bioeconomic framework that incorporates decision-making in epidemiological models, taking into account feedbacks between the economic and epidemiological systems. This allows us to model how private individuals and disease control authorities separately evaluate economic and epidemiological tradeoffs (e.g. quarantines designed to reduce infection risks to livestock can cause ranchers to reduce biosecurity, thereby counteracting the public policies).

3. Spatial interaction models. We modeled the role of private property rights in providing incentives for spatially explicit infectious disease control, and found that higher rates of dispersal reduce the incentives for control. Where there is spatial heterogeneity, the control is a ‘weaker link’ public good. We investigated the scope for the allocation of rights to resolve the problem in this case.

4. Trade and disease models. We modeled the interactions between importers, border protection agencies and the transmission of infectious animal diseases. Taking Foot and Mouth as an example of a disease managed through trade interdiction, we show the effect of trade decisions, inspection and interception regimes, and endemic status on disease outbreaks and spread.

Applied studies of trade, travel and disease Zoonoses are responsible for the majority of emerging infectious diseases in the last four decades, and the proportion of those originating in wildlife is growing. Four main lines of inquiry are being pursued.

1. Wildlife pet trade. We have begun to analyze the trade in wildlife for pets into the USA and the cost of alternative strategies of captive breeding, and have begun an analysis of the damages due to emerging pandemics originating wildlife, and the cost of prevention via interventions in trade and travel. We aim to assess the viability of alternative mechanisms for reducing the risk of disease introduction.

2. Migratory waterbirds. We have built a global dataset of the summer and winter distributions and migratory pathways for each of 2280 subspecies of migratory waterbirds, a group that plays a key role in the spread of avian influenza and other pathogens.

3. West Nile Virus. We have analyzed a decade of surveillance data for West Nile virus in 21 counties in Colorado (a disease hotspot). Early results suggest that human incidence of this disease varies both spatially and temporally at the county scale and is strongly linked to entomological risk (the density of infected mosquitoes). However, there is significant variation across counties in the intercepts and slopes of these relationships implying that human behavior differs spatially and temporally as well.

4. H1N1. We are investigating whether people adjust time spent in public places in response to the perceived risk of contracting H1N1, and whether avoidance behavior is stronger for higher-risk groups, such as children and the old. In Phoenix we have assembled 3000 micro level records describing individual time use, H1N1 activity, and internet traffic related to H1N1 between 2008 and 2010 (sourced from the American Time Use, the AZ Dept. of Health Services, and the Google Trends search engine). In Mexico City we are accessing data on changes in TV viewing between 2008 and 2010 as a proxy for changes in social distancing behavior. The data will be used to estimate the epidemiological model of disease dynamics with an extension that accounts for adaptive human response.

Project funding

MASpread is funded by the National Institute of General Medical Sciences (NIGMS) under a joint initiative between the NIGMS and the National Science Foundation's Division of Mathematical Sciences (DMS) to support research at the interface of the Biological and Mathematical Sciences.