Nowcasting the Spread of Chikungunya Virus in theAmericasMichael A. Johansson1*, Ann M. Powers2, Nicki Pesik3, Nicole J. Cohen3, J. Erin Staples21 Division of Vector-Borne Diseases, Centers for Diseases Control and Preven tion, San Juan, PR, 2 Division of Vector-Borne Diseases, Centers for Diseases Control andPrevention, Fort Collins, Colorado, United States of America, 3 Division of Global Migration and Quarantine, Centers for Diseases Control and Prevention, Atlanta, Georgia,United States of AmericaAbstractBackground:In December 2013, the first locally-acquired chikungunya virus (CHIKV) infections in the Americas werereported in the Caribbean. As of May 16, 55,992 cases had been reported and the outbreak was still spreading. Identificationof newly affected locations is paramount to intervention activities, but challenging due to limitations of current data on theoutbreak and on CHIKV transmission. We developed models to make probabilistic predictions of spread based on currentdata considering these limitations.Methods and Findings:Branching process models capturing travel patterns, local infection prevalence, climate dependenttransmission factors, and associated uncertainty estimates were developed to predict probable locations for the arrival ofCHIKV-infected travelers and for the initiation of local transmission. Many international cities and areas close to wheretransmission has already occurred were likely to have received infected travelers. Of the ten locations predicted to be themost likely locations for introduced CHIKV transmission in the first four months of the outbreak, eight had reported localcases by the end of April. Eight additional locations were likely to have had introduction leading to local transmission inApril, but with substantial uncertainty.Conclusions:Branching process models can characterize the risk of CHIKV introduction and spread during the ongoingoutbreak. Local transmission of CHIKV is currently likely in several Caribbean locations and possible, though uncertain, forother locations in the continental United States, Central America, and South America. This modeling framework may also beuseful for other outbreaks where the risk of pathogen spread over heterogeneous transportation networks must be rapidlyassessed on the basis of limited information.Citation: Johansson MA, Powers AM, Pesik N, Cohen NJ, Staples JE (2014) Nowcasting the Spread of Chikungunya Virus in the Americas. PLoS ONE 9(8): e104915.doi:10.1371/journal.pone.0104915Editor: Lisa F.P. Ng, Singapore Immunology Network, Agency for Science, Technology and Research (A*STAR), SingaporeReceived May 20, 2014; Accepted July 3, 2014; Published August 11, 2014This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone forany lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.Data Availability: The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. Case data are availablefrom the the Pan American Health Organization (http://www.paho.org) and the French Institute for Public Health Surveillance (http://www.invs.sante.fr/Actualites/Points-epidemiologiques). Climate data are available from NCAR/NOAA (www.esrl.noaa.gov/psd/data/reanaly sis). Flight data are available from Data In,Intelligence Out (www.diio.net).Funding: The authors have no support or funding to report.Competing Interests: The authors have decl ared that no competing interests exist.* Email: [email protected] December 2013, the first locally-acquired chikungunya virus(CHIKV) infections in the Americas were reported from St.Martin [1]. CHIKV is transmitted to humans by Aedes aegyptiand Ae. albopictus mosquitoes and can cause explosive outbreaksof fever and severe polyarthralgia affecting 30–75% of thepopulation [2,3,4]. Prior to 2013, outbreaks of chikungunya hadbeen reported in Africa, Asia, Europe, and islands in the Indianand Pacific Oceans. While CHIKV transmission had never beendocumented in the Americas, the potential for outbreaks had longbeen recognized because of the prevalence of the vectors and theirefficiency at transmitting dengue viruses [5].As of May 16, 55,992 locally acquired and travel-related caseshad been reported from fourteen islands in the Caribbean andFrench Guiana [6]. Although further spread is probable, thecurrent extent of spread and risk is uncertain. Uncertainty arisesfrom numerous factors including challenges in assessing thecurrent prevalence of infection and travel patterns, the complexityof the transmission cycle, and stochasticity in outbreak propaga-tion. Measuring the prevalence of CHIKV is challenging as casesmight be unrecognized, confused with other diseases such asdengue, or not reported. Travel patterns are also difficult tocapture in real-time and might change due to the outbreak itself.Transmission potential is difficult to predict due to differences inmosquito species, vector competence, and vector densities[7,8,9,10,11,12,13]. Lastly, epidemics are inherently stochastic;there may be numerous possible routes of spread, but by chanceonly some will actual occur. Given the many unknown entities,models considering both the available data and the associateduncertainty can provide insight on the most probable routes ofspread and the locations where unrecognized cases may already beoccurring.PLOS ONE | www.plosone.org 1 August 2014 | Volume 9 | Issue 8 | e104915To estimate the current risk of CHIKV spread, we utilized twobranching process models [14]. The first model estimates theprobability of at least one CHIKV infected traveler arrivingsomewhere as a single step process dependent on (1) the number ofinfected individuals in locations where transmission has occurred,(2) the probability of those individuals travelling, and (3) theduration of infection. The second model estimates the probabilityof CHIKV transmission spreading to new locations as a three-stepprocess: (1) an infected traveler must arrive; (2) that traveler mustinfect at least one mosquito; and (3) at least one infected mosquitomust infect at least one person. We incorporated uncertainties intothese models using global sensitivity analysis and predicted theprobability of infected travelers and the initiation of autochtho-nous transmission for each of the first five months of the outbreak(December 2013–April
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