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   Comparative Analysis of Dengue and Zika Outbreaks Reveals Differences by Setting and Virus  
   
نویسنده funk s. ,kucharski a.j. ,camacho a. ,eggo r.m. ,yakob l. ,murray l.m. ,edmunds w.j.
منبع plos neglected tropical diseases - 2016 - دوره : 10 - شماره : 12
چکیده    The pacific islands of micronesia have experienced several outbreaks of mosquito-borne diseases over the past decade. in outbreaks on small islands,the susceptible population is usually well defined,and there is no co-circulation of pathogens. because of this,analysing such outbreaks can be useful for understanding the transmission dynamics of the pathogens involved,and particularly so for yet understudied pathogens such as zika virus. here,we compared three outbreaks of dengue and zika virus in two different island settings in micronesia,the yap main islands and fais,using a mathematical model of transmission dynamics and making full use of commonalities in disease and setting between the outbreaks. we found that the estimated reproduction numbers for zika and dengue were similar when considered in the same setting,but that,conversely,reproduction number for the same disease can vary considerably by setting. on the yap main islands,we estimated a reproduction number of 8.0–16 (95% credible interval (ci)) for the dengue outbreak and 4.8–14 (95% ci) for the zika outbreak,whereas for the dengue outbreak on fais our estimate was 28–102 (95% ci). we further found that the proportion of cases of zika reported was smaller (95% ci 1.4%–1.9%) than that of dengue (95% ci: 47%–61%). we confirmed these results in extensive sensitivity analysis. they suggest that models for dengue transmission can be useful for estimating the predicted dynamics of zika transmission,but care must be taken when extrapolating findings from one setting to another. © 2016 funk et al.
آدرس centre for the mathematical modelling of infectious diseases,london school of hygiene & tropical medicine,london,united kingdom,department of infectious disease epidemiology,london school of hygiene & tropical medicine,london, United Kingdom, centre for the mathematical modelling of infectious diseases,london school of hygiene & tropical medicine,london,united kingdom,department of infectious disease epidemiology,london school of hygiene & tropical medicine,london, United Kingdom, centre for the mathematical modelling of infectious diseases,london school of hygiene & tropical medicine,london,united kingdom,department of infectious disease epidemiology,london school of hygiene & tropical medicine,london, United Kingdom, centre for the mathematical modelling of infectious diseases,london school of hygiene & tropical medicine,london,united kingdom,department of infectious disease epidemiology,london school of hygiene & tropical medicine,london, United Kingdom, centre for the mathematical modelling of infectious diseases,london school of hygiene & tropical medicine,london,united kingdom,department for disease control,london school of hygiene & tropical medicine,london, United Kingdom, department of statistics,university of oxford,oxford, United Kingdom, centre for the mathematical modelling of infectious diseases,london school of hygiene & tropical medicine,london,united kingdom,department of infectious disease epidemiology,london school of hygiene & tropical medicine,london, United Kingdom
 
     
   
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