International journal of MCH and AIDS
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The population with emerging diseases such as COVID-19, which is used to calculate the basic reproduction number of epidemic outbreak (R0 ) cannot be simply observed. In this article, we have proposed a method for estimating the hidden population of people with COVID-19 disease. ⋯ The provision of medical equipment (e.g., masks, alcohol, ventilators, medication, etc.), the reopening of schools and universities, the start of tourism and public gatherings, the provision of medical staff and preventive planning depend on the number of patients with the disease. Therefore, it is very important to estimate the number of patients.
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The world is currently witnessing a dramatic disruption of everyday life owing to the rapid progression of the coronavirus disease 2019 (COVID-19) pandemic. As the pandemic evolves, there is an urgent need to better understand its epidemiology, characterize its potential impact, and identify mitigatory strategies to avert pandemic-related mortality. There is a need for a tool or algorithm to evaluate the extent to which public health policy and/or economic preparedness measures are effectively averting COVID-19 related mortality. ⋯ Using the PEI and current COVID-19-related mortality, we determined that so far Germany demonstrates the highest PEI (5.1) among countries with more than 5,000 recorded cases of the infection, indicating high quality measures instituted by the country to avert death during the pandemic. Italy and France currently have the lowest COVID-19-related PEIs. Epidemics and pandemics come and go, but local, national, and global abilities to determine the efficiency of their efforts in averting deaths is critical.
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We examined the trends in stillbirth across gestational age in the United States (US). We conducted a trend analysis using the U. S. ⋯ We compared the incidence and rates of stillbirth for term, all preterm, moderate-to-late preterm, very preterm, and extreme preterm phenotypes. The incidence of stillbirth decreased for the entire birth cohort over the 36-year period. The rates of overall, term, all preterm, very preterm and moderate-to-late preterm stillbirth decreased from 1982 to 2017; however, the rates for extreme preterm stillbirth increased by about 7.6% over the same study period.
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Artificial Intelligence (AI) applications in medicine have grown considerably in recent years. AI in the forms of Machine Learning, Natural Language Processing, Expert Systems, Planning and Logistics methods, and Image Processing networks provide great analytical aptitude. While AI methods were first conceptualized for radiology, investigations today are established across all medical specialties. ⋯ Other promising AI tools have demonstrated an ability to: predict burn healing time from smartphone photos; track regions of socioeconomic disparity combined with environmental trends to predict communicable disease outbreaks; and accurately predict pregnancy complications such as birth asphyxia in low resource settings with limited patient clinical data. In this commentary, we discuss the current state of AI-driven GHI and explore relevant lessons from past technology-centered GHI. Additionally, we propose a conceptual framework to guide the development of sustainable strategies for AI-driven GHI, and we outline areas for future research.
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As the global impact of the COVID-19 pandemic continues to evolve, robust data describing its effect on maternal and child health (MCH) remains limited. The aim of this study was to elucidate an agenda for COVID-19 research with particular focus on its impact within MCH populations. ⋯ Proposed research topics included vaccine development, genomics, and artificial intelligence among others. The proposed research priorities could serve as a template for a vigorous COVID-19 research agenda by the NIH and other national funding agencies in the US.