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BACKGROUND: The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico. METHODS: To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models. RESULTS: We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases. CONCLUSION: The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico.

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BACKGROUND: Pregnancy increases a woman's risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico. METHOD: Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status. RESULTS: Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype. CONCLUSION: The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico.

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Background: The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico. Methods: We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters. Results: DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence. Conclusions: Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.

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Surveillance is a critical component of any dengue prevention and control program. There is an increasing effort to use drones in mosquito control surveillance. Due to the novelty of drones, data are scarce on the impact and acceptance of their use in the communities to collect health-related data. The use of drones raises concerns about the protection of human privacy. Here, we show how willingness to be trained and acceptance of drone use in tech-savvy communities can help further discussions in mosquito surveillance. A cross-sectional study was conducted in Malaysia, Mexico, and Turkey to assess knowledge of diseases caused by Aedes mosquitoes, perceptions about drone use for data collection, and acceptance of drones for Aedes mosquito surveillance around homes. Compared with people living in Turkey, Mexicans had 14.3 (p < 0.0001) times higher odds and Malaysians had 4.0 (p = 0.7030) times the odds of being willing to download a mosquito surveillance app. Compared to urban dwellers, rural dwellers had 1.56 times the odds of being willing to be trained. There is widespread community support for drone use in mosquito surveillance and this community buy-in suggests a potential for success in mosquito surveillance using drones. A successful surveillance and community engagement system may be used to monitor a variety of mosquito spp. Future research should include qualitative interview data to add context to these findings.

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Arboviruses such as Chikungunya (CHIKV), Dengue (DENV), and Zika virus (ZIKV) have emerged as a significant public health concern in Mexico. The existing literature lacks evidence regarding the dispersion of arboviruses, thereby limiting public health policy's ability to integrate the diagnosis, management, and prevention. This study seeks to reveal the clinical symptoms of CHIK, DENV, and ZIKV by age group, region, sex, and time across Mexico. The confirmed cases of CHIKV, DENV, and ZIKV were compiled from January 2012 to March 2020. Demographic characteristics analyzed significant clinical symptoms of confirmed cases. Multinomial logistic regression was used to assess the association between clinical symptoms and geographical regions. Females and individuals aged 15 and older had higher rates of reported significant symptoms across all three arboviruses. DENV showed a temporal variation of symptoms by regions 3 and 5, whereas ZIKV presented temporal variables in regions 2 and 4. This study revealed unique and overlapping symptoms between CHIKV, DENV, and ZIKV. However, the differentiation of CHIKV, DENV, and ZIKV is difficult, and diagnostic facilities are not available in rural areas. There is a need for adequately trained healthcare staff alongside well-equipped lab facilities, including hematological tests and imaging facilities.