Nowcasting the Spread of Chikungunya Virus in the Americas
Publication Date
August 11, 2014
Journal
PLOS ONE
Authors
Michael A. Johansson, Ann M. Powers, Nicki Pesik, Nicole J. Cohen, et al
Volume
9
Issue
8
Pages
e104915
DOI
https://dx.plos.org/10.1371/journal.pone.0104915
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0104915
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25111394
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4128737
Europe PMC
http://europepmc.org/abstract/MED/25111394
Web of Science
000341105100109
Scopus
84905862542
Mendeley
http://www.mendeley.com/research/nowcasting-spread-chikungunya-virus-americas
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Mendeley | Further Information

{"title"=>"Nowcasting the spread of Chikungunya Virus in the Americas", "type"=>"journal", "authors"=>[{"first_name"=>"Michael A.", "last_name"=>"Johansson", "scopus_author_id"=>"16233749100"}, {"first_name"=>"Ann M.", "last_name"=>"Powers", "scopus_author_id"=>"7005770718"}, {"first_name"=>"Nicki", "last_name"=>"Pesik", "scopus_author_id"=>"6603160546"}, {"first_name"=>"Nicole J.", "last_name"=>"Cohen", "scopus_author_id"=>"25959939100"}, {"first_name"=>"J.", "last_name"=>"Erin Staples", "scopus_author_id"=>"12752937000"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"25111394", "doi"=>"10.1371/journal.pone.0104915", "sgr"=>"84905862542", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84905862542", "issn"=>"19326203", "pui"=>"373746867"}, "id"=>"fb51cfb2-e947-3aa4-9f82-05bc0ce068cc", "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.", "link"=>"http://www.mendeley.com/research/nowcasting-spread-chikungunya-virus-americas", "reader_count"=>115, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>29, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>24, "Student > Postgraduate"=>3, "Other"=>5, "Student > Master"=>20, "Student > Bachelor"=>14, "Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>6, "Librarian"=>1, "Researcher"=>29, "Student > Doctoral Student"=>3, "Student > Ph. D. 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CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1630495"], "description"=>"<p>Mean estimates (points) and 95% percentiles (lines) of predictions for the probability of introduced local transmission by month (December (D), January (J), February (F), March (M), and April (A)). <b>A.</b> Locations with reported autochthonous cases prior to May 2, 2104. The red points represent the month when cases were first reported. <b>B.</b> St. Lucia and Haiti had reported cases in early May. <b>C.</b> The eight locations with a predicted probability of local transmission greater than 0.5 in April and no history of cases as of May 16. BVI: British Virgin Islands; USVI: U.S. Virgin Islands.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "Plant science", "Plant pathology", "Infectious disease epidemiology", "epidemiology", "Infectious diseases", "Viral diseases", "Chikungunya infection", "Travel-associated diseases", "Mathematical and statistical techniques", "mathematical models", "Simulation and modeling", "mathematical modeling", "probabilities", "chikungunya", "december"], "article_id"=>1134941, "categories"=>["Biological Sciences"], "users"=>["Michael A. Johansson", "Ann M. Powers", "Nicki Pesik", "Nicole J. Cohen", "J. Erin Staples"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104915.g003", "stats"=>{"downloads"=>0, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Monthly_probabilities_of_local_transmission_of_chikungunya_virus_for_select_locations_December_2013_8211_April_2014_/1134941", "title"=>"Monthly probabilities of local transmission of chikungunya virus for select locations, December 2013–April 2014.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 03:05:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630496"], "description"=>"<p><b>A.</b> Location-specific mean estimates (points) and 95% percentiles (lines) of the cumulative predicted probabilities of local transmission for the 50 locations most likely to have had introduced transmission over the time period December 2013–April 2014. Locations which had reported locally-acquired cases as of May 2 are marked in red. <b>B.</b> Location-specific mean estimates (points) and 95% percentiles (lines) of the predicted probabilities for the 50 locations most likely to have had introduced transmission in April. <b>C.</b> The mean probabilities of local transmission for all locations in the Americas with p>0.1 in April. BVI: British Virgin Islands; USVI: U.S. Virgin Islands.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "Plant science", "Plant pathology", "Infectious disease epidemiology", "epidemiology", "Infectious diseases", "Viral diseases", "Chikungunya infection", "Travel-associated diseases", "Mathematical and statistical techniques", "mathematical models", "Simulation and modeling", "mathematical modeling", "chikungunya"], "article_id"=>1134942, "categories"=>["Biological Sciences"], "users"=>["Michael A. Johansson", "Ann M. Powers", "Nicki Pesik", "Nicole J. Cohen", "J. Erin Staples"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104915.g004", "stats"=>{"downloads"=>1, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_of_local_transmission_of_chikungunya_virus_by_location_/1134942", "title"=>"Probability of local transmission of chikungunya virus by location.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 03:05:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630493"], "description"=>"<p>Location-specific predictions of the probability of the arrival of at least one chikungunya infected traveler by month and cumulatively over the 5-month period. Locations shown have mean probabilities of importation greater than 0.1. Locations which had reported locally-acquired or travel-related cases in that month or previous months are marked in red.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "Plant science", "Plant pathology", "Infectious disease epidemiology", "epidemiology", "Infectious diseases", "Viral diseases", "Chikungunya infection", "Travel-associated diseases", "Mathematical and statistical techniques", "mathematical models", "Simulation and modeling", "mathematical modeling", "chikungunya", "importation", "december"], "article_id"=>1134939, "categories"=>["Biological Sciences"], "users"=>["Michael A. Johansson", "Ann M. Powers", "Nicki Pesik", "Nicole J. Cohen", "J. Erin Staples"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104915.g001", "stats"=>{"downloads"=>3, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_of_chikungunya_virus_importation_by_location_December_2013_8211_April_2014_/1134939", "title"=>"Probability of chikungunya virus importation by location, December 2013–April 2014.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 03:05:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630494"], "description"=>"<p>Location-specific mean estimates (points) and 95% percentiles (lines) for the predicted probability of the arrival of at least one chikungunya infected traveler for the 50 locations most likely to have had imported cases in April. USVI: U.S. Virgin Islands.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "Plant science", "Plant pathology", "Infectious disease epidemiology", "epidemiology", "Infectious diseases", "Viral diseases", "Chikungunya infection", "Travel-associated diseases", "Mathematical and statistical techniques", "mathematical models", "Simulation and modeling", "mathematical modeling", "chikungunya", "importation", "april"], "article_id"=>1134940, "categories"=>["Biological Sciences"], "users"=>["Michael A. Johansson", "Ann M. Powers", "Nicki Pesik", "Nicole J. Cohen", "J. Erin Staples"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104915.g002", "stats"=>{"downloads"=>1, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_of_chikungunya_virus_importation_for_select_locations_April_2014_/1134940", "title"=>"Probability of chikungunya virus importation for select locations, April 2014.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 03:05:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630497"], "description"=>"<div><p>Background</p><p>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.</p><p>Methods and Findings</p><p>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.</p><p>Conclusions</p><p>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.</p></div>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "Infectious disease modeling", "Plant science", "Plant pathology", "Infectious disease epidemiology", "epidemiology", "Infectious diseases", "Viral diseases", "Chikungunya infection", "Travel-associated diseases", "Mathematical and statistical techniques", "mathematical models", "Simulation and modeling", "mathematical modeling", "chikungunya"], "article_id"=>1134943, "categories"=>["Biological Sciences"], "users"=>["Michael A. Johansson", "Ann M. Powers", "Nicki Pesik", "Nicole J. Cohen", "J. Erin Staples"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104915", "stats"=>{"downloads"=>2, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Nowcasting_the_Spread_of_Chikungunya_Virus_in_the_Americas_/1134943", "title"=>"Nowcasting the Spread of Chikungunya Virus in the Americas", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-11 03:05:18"}

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