Identifying urban hotspots of dengue, chikungunya and Zika transmission in Mexico to support risk stratification efforts

Dataset to support publication "Identifying urban hotspots of dengue, chikungunya and Zika transmission in Mexico to support risk stratification efforts

Identifying urban hotspots of dengue, chikungunya and Zika transmission in Mexico to support risk stratification efforts

Dataset to support publication "Identifying urban hotspots of dengue, chikungunya and Zika transmission in Mexico to support risk stratification efforts

Long-lasting insecticide-treated house screens and targeted treatment of productive breeding-sites for dengue vector control in Acapulco, Mexico

Background Long-lasting insecticidal net screens (LLIS) fitted to domestic windows and doors in combination with targeted treatment (TT) of the most productive Aedes aegypti breeding sites were evaluated for their impact on dengue vector indices in a cluster-randomised trial in Mexico between 2011 and 2013. Methods Sequentially over 2 years, LLIS and TT were deployed in 10 treatment clusters (100 houses/cluster) and followed up over 24 months. Cross-sectional surveys quantified infestations of adult mosquitoes, immature stages at baseline (pre-intervention) and in four post-intervention samples at 6-monthly intervals. Identical surveys were carried out in 10 control clusters that received no treatment. Results LLIS clusters had significantly lower infestations compared to control clusters at 5 and 12 months after installation, as measured by adult (male and female) and pupal-based vector indices. After addition of TT to the intervention houses in intervention clusters, indices remained significantly lower in the treated clusters until 18 (immature and adult stage indices) and 24 months (adult indices only) post-intervention. Conclusions These safe, simple affordable vector control tools were well-accepted by study participants and are potentially suitable in many regions at risk from dengue worldwide

Spatio-temporal coherence of dengue, chikungunya and Zika outbreaks in Merida, Mexico

Response to Zika virus (ZIKV) invasion in Brazil lagged a year from its estimated February 2014 introduction, and was triggered by the occurrence of severe congenital malformations. Dengue (DENV) and chikungunya (CHIKV) invasions tend to show similar response lags. We analyzed geo-coded symptomatic case reports from the city of Merida, Mexico, with the goal of assessing the utility of historical DENV data to infer CHIKV and ZIKV introduction and propagation. About 42% of the 40,028 DENV cases reported during 2008–2015 clustered in 27% of the city, and these clustering areas were where the first CHIKV and ZIKV cases were reported in 2015 and 2016, respectively. Furthermore, the three viruses had significant agreement in their spatio-temporal distribution (Kendall W>0.63; p≺0.01). Longitudinal DENV data generated patterns indicative of the resulting introduction and transmission patterns of CHIKV and ZIKV, leading to important insights for the surveillance and targeted control to emerging Aedes-borne viruses

Spatio-temporal coherence of dengue, chikungunya and Zika outbreaks in Merida, Mexico

Response to Zika virus (ZIKV) invasion in Brazil lagged a year from its estimated February 2014 introduction, and was triggered by the occurrence of severe congenital malformations. Dengue (DENV) and chikungunya (CHIKV) invasions tend to show similar response lags. We analyzed geo-coded symptomatic case reports from the city of Merida, Mexico, with the goal of assessing the utility of historical DENV data to infer CHIKV and ZIKV introduction and propagation. About 42% of the 40,028 DENV cases reported during 2008–2015 clustered in 27% of the city, and these clustering areas were where the first CHIKV and ZIKV cases were reported in 2015 and 2016, respectively. Furthermore, the three viruses had significant agreement in their spatio-temporal distribution (Kendall W>0.63; p≺0.01). Longitudinal DENV data generated patterns indicative of the resulting introduction and transmission patterns of CHIKV and ZIKV, leading to important insights for the surveillance and targeted control to emerging Aedes-borne viruses

Validation of DENV spatial clustering.

<p>(A) location of the residence of 505 children aged 8 or younger and who provided a blood sample in 2015, stratified by those residing inside (black) and outside (red) the 2008–2015 DENV hot-spot area. (B) Average DENV infection probability, estimated from a mixed-effects logistic regression model, for the children living inside (black solid line) or outside (red dotted line) the 2008–2015 DENV hot-spot area. Error bars indicate the standard error of the mean. Details of the model are shown in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006298#pntd.0006298.t001" target="_blank">Table 1</a>. Source of the census tract boundaries was INEGI, 2010.</p

Spatial pattern of arbovirus hot-spots in Merida.

<p>(A) Hot-spots of CHIKV (2015–2016) and ZIKV (2016) standardized case counts overlapped with persistent DENV clustering area (gray polygons represent persistent DENV clustering areas and red polygons represent statistically significant yearly hot-spots for CHIKV and ZIKV). (B) Q-Q plots comparing distribution of standardized case counts of DENV, CHIKV, and ZIKV. Source of the census tract boundaries was INEGI, 2010.</p

Spatio-temporal distribution of standardized dengue virus (DENV) cases in Merida, Mexico, during 2008–2015.

<p>(A) Weekly case counts are shown as bar plot (above) and by census tract (each row is a tract and rows are arranged by geographic proximity, with near units being next to each other, as shown in the color bar located on the right margin which matches the color map in (B)). Right panel shows total count of dengue cases per census unit during 2008–2015, which is mapped in (C). Panel (D) shows the frequency of outbreak periods in which each census unit was found to be a statistically significant hot-spot of DENV transmission. Panel (E) shows the same information for non-outbreak periods. Source of the census tract boundaries was Instituto Nacional de Estadística y Geografía (INEGI), 2010.</p