Background: The World Health Organization declared the ongoing Zika virus (ZIKV) epidemic in the Americas a Public Health Emergency of International Concern on February 1, 2016. ZIKV disease in humans is characterized by a “dengue-like” syndrome including febrile illness and rash. However, ZIKV infection in early pregnancy has been associated with severe birth defects, including microcephaly and other developmental issues. Mechanistic models of disease transmission can be used to forecast trajectories and likely disease burden but are currently hampered by substantial uncertainty on the epidemiology of the disease (e.g., the role of asymptomatic transmission, generation interval, incubation period, and key drivers). When insight is limited, phenomenological models provide a starting point for estimation of key transmission parameters, such as the reproduction number, and forecasts of epidemic impact. Methods: We obtained daily counts of suspected Zika cases by date of symptoms onset from the Secretary of Health of Antioquia, Colombia during January-April 2016. We calibrated the generalized Richards model, a phenomenological model that accommodates a variety of early exponential and sub-exponential growth kinetics, against the early epidemic trajectory and generated predictions of epidemic size. The reproduction number was estimated by applying the renewal equation to incident cases simulated from the fitted generalized-growth model and assuming gamma or exponentially-distributed generation intervals derived from the literature. We estimated the reproduction number for an increasing duration of the epidemic growth phase. Results: The reproduction number rapidly declined from 10.3 (95% CI: 8.3, 12.4) in the first disease generation to 2.2 (95% CI: 1.9, 2.8) in the second disease generation, assuming a gamma-distributed generation interval with the mean of 14 days and standard deviation of 2 days. The generalized-Richards model outperformed the logistic growth model and provided forecasts within 22% of the actual epidemic size based on an assessment 30 days into the epidemic, with the epidemic peaking on day 36. Conclusion: Phenomenological models represent promising tools to generate early forecasts of epidemic impact particularly in the context of substantial uncertainty in epidemiological parameters. Our findings underscore the need to treat the reproduction number as a dynamic quantity even during the early growth phase, and emphasize the sensitivity of reproduction number estimates to assumptions on the generation interval distribution.

Background: Low testing rates and delays in reporting hinder the estimation of the mortality burden associated with the COVID-19 pandemic. During a public health emergency, estimating all cause excess deaths above an expected level of death can provide a more reliable picture of the mortality burden. Here, we aim to estimate the absolute and relative mortality impact of COVID-19 pandemic in Mexico. Methods: We obtained weekly mortality time series due to all causes for Mexico, and by gender, and geographic region from 2015 to 2020. We also compiled surveillance data on COVID-19 cases and deaths to assess the timing and intensity of the pandemic and assembled weekly series of the proportion of tweets about ‘death’ from Mexico to assess the correlation between people’s media interaction about ‘death’ and the rise in pandemic deaths. We estimated all-cause excess mortality rates and mortality rate ratio increase over baseline by fitting Serfling regression models and forecasted the total excess deaths for Mexico for the first 4 weeks of 2021 using the generalized logistic growth model. Results: We estimated the all-cause excess mortality rate associated with the COVID-19 pandemic in Mexico in 2020 at 26.10 per 10,000 population, which corresponds to 333,538 excess deaths. Males had about 2-fold higher excess mortality rate (33.99) compared to females (18.53). Mexico City reported the highest excess death rate (63.54) and RR (2.09) compared to rest of the country (excess rate = 23.25, RR = 1.62). While COVID-19 deaths accounted for only 38.64% of total excess deaths in Mexico, our forecast estimate that Mexico has accumulated a total of ~ 61,610 [95% PI: 60,003, 63,216] excess deaths in the first 4 weeks of 2021. Proportion of tweets was significantly correlated with the excess mortality (ρ = 0.508 [95% CI: 0.245, 0.701], p-value = 0.0004). Conclusion: The COVID-19 pandemic has heavily affected Mexico. The lab-confirmed COVID-19 deaths accounted for only 38.64% of total all cause excess deaths (333,538) in Mexico in 2020. This reflects either the effect of low testing rates in Mexico, or the surge in number of deaths due to other causes during the pandemic. A model-based forecast indicates that an average of 61,610 excess deaths have occurred in January 2021.

Asia and its Hindu Kush Himalayan (HKH) region is particularly vulnerable to environmental change, especially climate and land use changes further influenced by rapid population growth, high level of poverty and unsustainable development. Asia has been a hotspot of dengue fever and chikungunya mainly due to its dense human population, unplanned urbanization and poverty. In an urban cycle, dengue virus (DENV) and chikungunya virus (CHIKV) are transmitted by Aedes aegypti and Ae. albopictus mosquitoes which are also competent vectors of Zika virus (ZIKV). Over the last decade, DENV and CHIKV transmissions by Ae. aegypti have extended to the Himalayan countries of Bhutan and Nepal and ZIKV could follow in the footsteps of these viruses in the HKH region. The already established distribution of human-biting Aedes mosquito vectors and a naïve population with lack of immunity against ZIKV places the HKH region at a higher risk of ZIKV. Some of the countries in the HKH region have already reported ZIKV cases. We have documented an increasing threat of ZIKV in Asia and its HKH region because of the high abundance and wide distribution of human-biting mosquito vectors, climate change, poverty, report of indigenous cases in the region, increasing numbers of imported cases and a naïve population with lack of immunity against ZIKV. An outbreak anywhere is potentially a threat everywhere. Therefore, in order to ensure international health security, all efforts to prevent, detect, and respond to ZIKV ought to be intensified now in Asia and its HKH region. To prepare for possible ZIKV outbreaks, Asia and the HKH region can also learn from the success stories and strategies adopted by other regions and countries in preventing ZIKV and associated complications. The future control strategies for DENV, CHIKV and ZIKV should be considered in tandem with the threat to human well-being that is posed by other emerging and re-emerging vector-borne and zoonotic diseases, and by the continuing urgent need to strengthen public primary healthcare systems in the region.

Background. This study examines the association between mental disorders and neighborhood conditions in a nationally representative sample of US children. Methods. Data from US children aged 6–17 years (N=95,677) were obtained from the 2011/12 National Survey of Children’s Health. Analysis examined neighborhood conditions and demographic and psychosocial characteristics including adverse childhood experiences (ACEs), parental mental health status, and the associations with any current diagnosed mental disorders (ACDMD). ACDMD was a composite variable derived from four childhood mental disorders examined. We computed descriptive statistics and logistic regression analyses. Results. Approximately 14% children had ACDMD. Of the neighborhood factors examined, nonsupportive neighborhood (AOR 1.37, 95% CI: 1.10, 1.71) was significantly associated with ACDMD in the multivariable models. Similarly, mother’s mental health (AOR 1.84, 95% CI: 1.39, 2.43) and ACEs (e.g., AOR for 5–9 ACEs 6.36, 95% CI: 4.67, 8.65) were also found to be strongly associated with mental disorders. Conclusion. Our findings show that parental poor mental health, living in a nonsupportive neighborhood, and ACEs were important risk factors for child mental disorders. While more research is needed, children who have had early trauma and who reside with parents and caretakers with poor mental health are in need of additional services and treatment.

This study examined how socio-demographic, climate and population health characteristics shaped the geospatial variability in excess mortality patterns during the COVID-19 pandemic in Mexico. We used Serfling regression models to estimate all-cause excess mortality rates for all 32 Mexican states. The association between socio-demographic, climate, health indicators and excess mortality rates were determined using multiple linear regression analyses. Functional data analysis characterized clusters of states with distinct excess mortality growth rate curves. The overall all-cause excess deaths rate during the COVID-19 pandemic in Mexico until April 10, 2021 was estimated at 39.66 per 10 000 population. The lowest excess death rates were observed in southeastern states including Chiapas (12.72) and Oaxaca (13.42), whereas Mexico City had the highest rate (106.17), followed by Tlaxcala (51.99). We found a positive association of excess mortality rates with aging index, marginalization index, and average household size (P < 0.001) in the final adjusted model (Model R²=77%). We identified four distinct clusters with qualitatively similar excess mortality curves. Central states exhibited the highest excess mortality rates, whereas the distribution of aging index, marginalization index, and average household size explained the variability in excess mortality rates across Mexico.