Molecular and epidemiologic characterization of the diphtheria outbreak in Venezuela.

In 2016, Venezuela faced a large diphtheria outbreak that extended until 2019. Nasopharyngeal or oropharyngeal samples were prospectively collected from 51 suspected cases and retrospective data from 348 clinical records was retrieved from 14 hospitals between November 2017 and November 2018. Confirmed pathogenic Corynebactrium isolates were biotyped. Multilocus Sequence Typing (MLST) was performed followed by next-generation-based core genome-MLST and minimum spanning trees were generated. Subjects between 10 and 19 years of age were mostly affected (n = 95; 27.3%). Case fatality rates (CFR) were higher in males (19.4%), as compared to females (15.8%). The highest CFR (31.1%) was observed among those under 5, followed by the 40 to 49 age-group (25.0%). Nine samples corresponded to C. diphtheriae and 1 to C. ulcerans. Two Sequencing Types (ST), ST174 and ST697 (the latter not previously described) were identified among the eight C. diphtheriae isolates from Carabobo state. Cg-MLST revealed only one cluster also from Carabobo. The Whole Genome Sequencing analysis revealed that the outbreak seemed to be caused by different strains with C. diphtheriae and C. ulcerans coexisting. The reemergence and length of this outbreak suggest vaccination coverage problems and an inadequate control strategy.

Investigating the utility of Google trends for Zika and Chikungunya surveillance in Venezuela.

INTRODUCTION: Chikungunya and Zika Virus are vector-borne diseases responsible for a substantial disease burden in the Americas. Between 2013 and 2016, no cases of Chikungunya or Zika Virus were reported by the Venezuelan Ministry of Health. However, peaks of undiagnosed fever cases have been observed during the same period. In the context of scarce data, alternative surveillance methods are needed. Assuming that unusual peaks of acute fever cases correspond to the incidences of both diseases, this study aims to evaluate the use of Google Trends as an indicator of the epidemic behavior of Chikungunya and Zika. METHODS: Time-series cross-correlations of acute fever cases reported by the Venezuelan Ministry of Health and data on Google search queries related to Chikungunya and Zika were calculated. RESULTS: A temporal distinction has been made so that acute febrile cases occurring between 25th of June 2014 and 23rd of April 2015 were attributed to the Chikungunya virus, while cases occurring between 30th of April 2015 and 29th of April 2016 were ascribed to the Zika virus. The highest cross-correlations for each disease were shown at a lag of 0 (r = 0.784) for Chikungunya and at + 1 (r = 0.754) for Zika. CONCLUSION: The strong positive correlation between Google search queries and official data on acute febrile cases suggests that this resource can be used as an indicator of endemic urban arboviruses activity. In the Venezuelan context, Internet search queries might help to overcome some of the gaps that exist in the national surveillance system.

Google dengue trends: An indicator of epidemic behavior. The Venezuelan Case.

INTRODUCTION: Dengue Fever is a neglected increasing public health thread. Developing countries are facing surveillance system problems like delay and data loss. Lately, the access and the availability of health-related information on the internet have changed what people seek on the web. In 2004 Google developed Google Dengue Trends (GDT) based on the number of search terms related with the disease in a determined time and place. The goal of this review is to evaluate the accuracy of GDT in comparison with traditional surveillance systems in Venezuela. METHODS: Weekly epidemic data from GDT, Official Reported Cases (ORC) and Expected Cases (EC) according the Ministry of Health (MH) was obtained Monthly and yearly correlation between GDT and ORC from 2004 until 2014 was obtained. Linear regressions taking the reported cases as dependent variable were calculated. RESULTS: The overall Pearson correlation between GDT and ORC was r=0.87 (p <0.001), while between ORC and EC according the Ministry of Health (MH) was r=0.33 (p<0.001). After clustering data in epidemic and non-epidemic weeks in comparison with GDT correlation were r=0.86 (p<0.001) and r=0.65 (p <0.001) respectively. Important interannual variation of the epidemic was observed. The model shows a high accuracy in comparison with the EC, particularly when the incidence of the disease is higher. CONCLUSIONS: This early warning tool can be used as an indicator for other communicable diseases in order to apply effective and timely public health measures especially in the setting of weak surveillance systems.