The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.

The emergence and rapid global spread of the severe acute respiratory syndrome (SARS) coronavirus in 2002-2003 prompted efforts by modelers to characterize SARS epidemiology and inform control policies. We overview and discuss models for emerging infectious diseases (EIDs), provide a critical survey of SARS modeling literature, and discuss promising future directions for research. We reconcile discrepancies between published estimates of the basic reproductive number R0 for SARS (a crucial epidemiologic parameter), discuss insights regarding SARS control measures that have emerged uniquely from a modeling approach, and argue that high priorities for future modeling of SARS and similar respiratory EIDs should include informing quarantine policy and better understanding the impact of population heterogeneity on transmission patterns.

Migration primarily influences HIV spread by increasing high-risk sexual behaviour, rather than by connecting areas of low and high risk. Frequent return of migrants is an important risk factor when coupled with increased sexual risk behaviour. Accordingly, intervention programmes in South Africa need to target the sexual behaviour of short-term migrants specifically, even though these individuals may be more difficult to identify.

Rural-urban migration does not appear to be responsible for maintaining the high HIV prevalence in rural Zimbabwe, but rates of HIV infection may be affected by rural-rural migration.

This study was undertaken to determine if acidic or basic fibroblast growth factor (FGF1 or FGF2) or vascular endothelial growth factor (VEGF) alters the radiation response of small bowel after total-body irradiation (TBI). Female C3H mice were treated with various doses of angiogenic growth factor administered intravenously 24 h before or 1 h after TBI. Radiation doses ranged from 7 to 18 Gy. End points measured were the number of crypts in three portions of the small bowel, the frequency of apoptosis of crypt cells at various times after TBI, and the LD50/30 (bone marrow syndrome) and LD50/6 (GI syndrome). Fibroblast growth factors alone, without TBI, decreased the number of crypts per circumference significantly. Among the factors tested, FGF2 caused the greatest decline in baseline crypt number. Despite this decrease in the baseline crypt number, after irradiation the number of surviving crypts was greater in animals treated with growth factor. The greatest radioprotection occurred at intermediate doses of growth factor (6 to 18 pg/mouse). Mice treated with FGF1 and FGF2 had crypt survival curves with a slope that was more shallow than that for saline-treated animals, indicating radiation resistance of crypt stem cells in FGF-treated mice. The LD50/6 was increased by approximately 10% for all treatments with angiogenic growth factors, whether given before or after TBI. Apoptosis of crypt cells was maximum at 4 to 8 h after TBI. The cumulative apoptosis was decreased significantly in animals treated with angiogenic growth factors, and the greatest protection against apoptosis was seen in animals treated with FGF2 prior to TBI. All three angiogenic growth factors tested were radioprotective in small bowel whether given 24 h before or 1 h after irradiation. The mechanism of protection is unlikely to involve proliferation of crypt stem cells, but probably does involve prevention of radiation-induced apoptosis or enhanced repair of DNA damage of crypt cells.

The Zika virus (ZIKV) outbreak in the Americas has caused global concern that we may be on the brink of a healthcare crisis. The lack of research on ZIKV in the over 60 years that we have known about it has left us with little in the way of starting points for drug discovery. Our response can build on previous efforts with virus outbreaks and lean heavily on work done on other flaviviruses such as dengue virus. We provide some suggestions of what might be possible and propose an open drug discovery effort that mobilizes global science efforts and provides leadership, which thus far has been lacking. We also provide a listing of potential resources and molecules that could be prioritized for testing as in vitro assays for ZIKV are developed. We propose also that in order to incentivize drug discovery, a neglected disease priority review voucher should be available to those who successfully develop an FDA approved treatment. Learning from the response to the ZIKV, the approaches to drug discovery used and the success and failures will be critical for future infectious disease outbreaks.

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Odds of survival were greatest when first Ebola virus–positive blood sample collected had low viral load.