We determined abundance of Aedes aegypti mosquitoes and presence of dengue virus (DENV) in females collected from premises of laboratory-confirmed dengue patients over a 12-month period (March 2007 to February 2008) in Merida, Mexico. Backpack aspiration from 880 premises produced 1,836 females and 1,292 males indoors (predominantly from bedrooms) and 102 females and 108 males from patios/backyards. The mean weekly indoor catch rate per home peaked at 7.8 females in late August. Outdoor abundances of larvae or pupae were not predictive of female abundance inside the home. DENV-infected Ae. aegypti females were recovered from 34 premises. Collection of DENV-infected females from homes of dengue patients up to 27 days after the onset of symptoms (median, 14 days) shows the usefulness of indoor insecticide application in homes of suspected dengue patients to prevent their homes from becoming sources for dispersal of DENV by persons visiting and being bitten by infected mosquitoes.

Abstract. The home, or domicile, is the principal environment for transmission of dengue virus (DENV) between humans and mosquito vectors. Community-wide distribution of insecticide-treated curtains (ITCs), mimicking vector control program-driven interventions, has shown promise to reduce DENV infections. We conducted a Casa Segura consumer product intervention study in Mérida, Mexico to determine the potential to reduce intradomicillary DENV transmission through ITC use in individual homes. Dengue virus infections in mosquitoes and in humans were reduced in homes with ITCs in one of two study subareas. Overall, ITCs reduced intradomicillary DENV transmission; ITC homes were significantly less likely to experience multiple DENV infections in humans than NTC homes. Dengue virus-infected Aedes aegypti females were reduced within the ITC homes where curtain use was highest. Some homes yielded up to nine infected Ae. aegypti females. This study provides insights regarding best practices for Casa Segura interventions to protect homes from intradomicillary DENV transmission.

Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.

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Background In Mexico, the COVID-19 pandemic led to preventative measures such as confinement and social interaction limitations that paradoxically may have aggravated healthcare access disparities for pregnant women and accentuated health system weaknesses addressing high-risk patients’ pregnancies. Our objective is to estimate the maternal mortality ratio in 1 year and analyze the clinical course of pregnant women hospitalized due to acute respiratory distress syndrome and COVID-19. Methods A retrospective surveillance study of the national maternal mortality was performed from February 2020–February 2021 in Mexico related to COVID-19 cases in pregnant women, including their outcomes. Comparisons were made between patients who died and those who survived to identify prognostic factors and underlying health conditions distribution. Results Maternal Mortality Ratio increased by 56.8% in the studied period, confirmed COVID-19 was the cause of 22.93% of cases. Additionally, unconfirmed cases represented 4.5% of all maternal deaths. Among hospitalized pregnant women with Acute Respiratory Distress Syndrome consistent with COVID-19, smoking and cardiovascular diseases were more common among patients who faced a fatal outcome. They were also more common in the age group of < 19 or > 38. In addition, pneumonia was associated with asthma and immune impairment, while diabetes and increased BMI increased the odds for death (Odds Ratio 2.30 and 1.70, respectively). Conclusions Maternal Mortality Ratio in Mexico increased over 60% in 1 year during the pandemic; COVID-19 was linked to 25.4% of maternal deaths in the studied period. Lethality among pregnant women with a diagnosis of COVID-19 was 2.8%, and while asthma and immune impairment increased propensity for developing pneumonia, obesity and diabetes increased the odds for in-hospital death. Measures are needed to improve access to coordinated well-organized healthcare to reduce maternal deaths related to COVID-19 and pandemic collateral effects.