Nowcasting the Spread of Chikungunya Virus in the Americas Michael A Johansson1 Ann M Powers2 Nicki Pesik3 Nicole J Cohen3 J Erin Staples2 1 Division of Vector Borne Diseases Centers for Diseases Control and Prevention San Juan PR 2 Division of Vector Borne Diseases Centers for Diseases Control and Prevention 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 America Abstract Background In December 2013 the first locally acquired chikungunya virus CHIKV infections in the Americas were reported in the Caribbean As of May 16 55 992 cases had been reported and the outbreak was still spreading Identification of newly affected locations is paramount to intervention activities but challenging due to limitations of current data on the outbreak and on CHIKV transmission We developed models to make probabilistic predictions of spread based on current data considering these limitations Methods and Findings Branching process models capturing travel patterns local infection prevalence climate dependent transmission factors and associated uncertainty estimates were developed to predict probable locations for the arrival of CHIKV infected travelers and for the initiation of local transmission Many international cities and areas close to where transmission has already occurred were likely to have received infected travelers Of the ten locations predicted to be the most likely locations for introduced CHIKV transmission in the first four months of the outbreak eight had reported local cases by the end of April Eight additional locations were likely to have had introduction leading to local transmission in April but with substantial uncertainty Conclusions Branching process models can characterize the risk of CHIKV introduction and spread during the ongoing outbreak Local transmission of CHIKV is currently likely in several Caribbean locations and possible though uncertain for other locations in the continental United States Central America and South America This modeling framework may also be useful for other outbreaks where the risk of pathogen spread over heterogeneous transportation networks must be rapidly assessed 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 0104915 Editor Lisa F P Ng Singapore Immunology Network Agency for Science Technology and Research A STAR Singapore Received May 20 2014 Accepted July 3 2014 Published August 11 2014 This is an open access article free of all copyright and may be freely reproduced distributed transmitted modified built upon or otherwise used by anyone for any 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 available from 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 reanalysis 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 declared that no competing interests exist Email mjohansson cdc gov from numerous factors including challenges in assessing the current prevalence of infection and travel patterns the complexity of the transmission cycle and stochasticity in outbreak propagation Measuring the prevalence of CHIKV is challenging as cases might be unrecognized confused with other diseases such as dengue or not reported Travel patterns are also difficult to capture in real time and might change due to the outbreak itself Transmission potential is difficult to predict due to differences in mosquito 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 chance only some will actual occur Given the many unknown entities models considering both the available data and the associated uncertainty can provide insight on the most probable routes of spread and the locations where unrecognized cases may already be occurring Introduction In 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 aegypti and Ae albopictus mosquitoes and can cause explosive outbreaks of fever and severe polyarthralgia affecting 30 75 of the population 2 3 4 Prior to 2013 outbreaks of chikungunya had been reported in Africa Asia Europe and islands in the Indian and Pacific Oceans While CHIKV transmission had never been documented in the Americas the potential for outbreaks had long been recognized because of the prevalence of the vectors and their efficiency at transmitting dengue viruses 5 As of May 16 55 992 locally acquired and travel related cases had been reported from fourteen islands in the Caribbean and French Guiana 6 Although further spread is probable the current extent of spread and risk is uncertain Uncertainty arises PLOS ONE www plosone org 1 August 2014 Volume 9 Issue 8 e104915 Nowcasting the Spread of Chikungunya Virus in the Americas The parameters are described in detail below Since some parameters L Q and c vary with temperature we used average monthly temperature data for the years 1993 2012 from the NOAA NCEP Reanalysis dataset www esrl noaa gov psd data reanalysis 16 to estimate location and month specific parameters To account for uncertainty in each parameter we sampled 10 000 sets of parameters from likely ranges of each For each location we estimated pIMPORT and pAUTO with all 10 000 parameter sets reporting the mean and the 2 5th and 97 5th percentiles of their distributions Figure S1 in File S1 shows the influence of this uncertainty and temperature on the predicted range of R0 the basic reproduction ratio Estimated R0 peaked at 5 2 at approximately 29uC with 50 of the values between 1 7 and 6 5 To estimate the current risk of CHIKV spread we utilized two branching process models 14 The first model estimates the probability of at least one CHIKV infected traveler arriving
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