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.

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.