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The coronavirus disease 2019 (COVID-19) has become a life-threatening pandemic. The epidemic trends in different countries vary considerably due to different policy-making and resources mobilization. We calculated basic reproduction number (R0) and the time-varying estimate of the effective reproductive number (Rt) of COVID-19 by using the maximum likelihood method and the sequential Bayesian method, respectively. European and North American countries possessed higher R0 and unsteady Rt fluctuations, whereas some heavily affected Asian countries showed relatively low R0 and declining Rt now. The numbers of patients in Africa and Latin America are still low, but the potential risk of huge outbreaks cannot be ignored. Three scenarios were then simulated, generating distinct outcomes by using SEIR (susceptible, exposed, infectious, and removed) model. First, evidence-based prompt responses yield lower transmission rate followed by decreasing Rt. Second, implementation of effective control policies at a relatively late stage, in spite of huge casualties at early phase, can still achieve containment and mitigation. Third, wisely taking advantage of the time-window for developing countries in Africa and Latin America to adopt adequate measures can save more people’s life. Our mathematical modeling provides evidence for international communities to develop sound design of containment and mitigation policies for COVID-19. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11684-020-0787-4 and is accessible for authorized users.

An outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV/SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R0 of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible, Exposed, Infectious, and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of Rt. In the first assumption, Rt was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with Rt = 1.9, 2.6, or 3.1. The number of infections would reach 11,044, 70,258, and 227,989, respectively, by 29 February 2020. In the second assumption, Rt was assumed to gradually decrease at different phases from high level of transmission (Rt = 3.1, 2.6, and 1.9) to below 1 (Rt = 0.9 or 0.5) owing to increasingly implemented public health intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,077–84,520 or 55,869–81,393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce Rt to an ideal level and control the infection.

Arsenic, an identified environmental toxicant, poses threats to the health of human beings through contaminated water and food. Recently, increasing reports focused on arsenic-induced nerve damage, however, the underlying mechanism remains elusive. Microglia are important immune cells in the nervous system, which produce a large number of inflammatory factors including TNF-α when activated. Recent reports indicated that TNF-α is involved in the process of necroptosis, a new type of programmed cell death discovered recently. Although there were evidences suggested that arsenic could induce both microglia activation and TNF-α production in the nervous system, the mechanism of arsenic-induced neurotoxicity due to microglia activation is rarely studied. In addition, the role of microglia-derived TNF-α in response to arsenic exposure in necroptosis has not been documented before. In this study, we found that arsenite induced microglial activation through p38 MAPK signaling pathway, leading to the production of TNF-α. Microglia-derived TNF-α further induced necroptosis in the neuronal cells. Our findings suggested that necroptosis induced by microglia-derived TNF-α upon arsenite exposure partially played a role in arsenic-induced cell death which underlie the fundamental event of arsenic-related neurotoxicity.

The impact of psychosocial factors on increasing the risk of HIV infection among men who have sex with men (MSM) has attracted increasing attention. We aimed to develop and validate an integrated prediction model, especially incorporating emerging psychosocial variables, for predicting the risk of HIV infection among MSM. We surveyed and collected sociodemographic, psychosocial, and behavioral information from 547 MSM in China. The participants were split into a training set and a testing set in a 3:1 theoretical ratio. The prediction model was constructed by introducing the important variables selected with the least absolute shrinkage and selection operator (LASSO) regression, applying multivariate logistic regression, and visually assessing the risk of HIV infection through the nomogram. Receiver operating characteristic curves (ROC), Kolmogorov–Smirnov test, calibration plots, Hosmer–Lemeshow test and population stability index (PSI) were performed to test validity and stability of the model. Four of the 15 selected variables—unprotected anal intercourse, multiple sexual partners, involuntary subordination and drug use before sex—were included in the prediction model. The results indicated that the comprehensive prediction model we developed had relatively good predictive performance and stability in identifying MSM at high-risk for HIV infection, thus providing targeted interventions for high-risk MSM.

A COVID-19 outbreak emerged in Wuhan, China at the end of 2019 and developed into a global pandemic during March 2020. The effects of temperature on the dynamics of the COVID-19 epidemic in China are unknown. Data on COVID-19 daily confirmed cases and daily mean temperatures were collected from 31 provincial-level regions in mainland China between Jan. 20 and Feb. 29, 2020. Locally weighted regression and smoothing scatterplot (LOESS), distributed lag nonlinear models (DLNMs), and random-effects meta-analysis were used to examine the relationship between daily confirmed cases rate of COVID-19 and temperature conditions. The daily number of new cases peaked on Feb. 12, and then decreased. The daily confirmed cases rate of COVID-19 had a biphasic relationship with temperature (with a peak at 10 °C), and the daily incidence of COVID-19 decreased at values below and above these values. The overall epidemic intensity of COVID-19 reduced slightly following days with higher temperatures with a relative risk (RR) was 0.96 (95% CI: 0.93, 0.99). A random-effect meta-analysis including 28 provinces in mainland China, we confirmed the statistically significant association between temperature and RR during the study period (Coefficient = -0.0100, 95% CI: -0.0125, -0.0074). The DLNMs in Hubei Province (outside of Wuhan) and Wuhan showed similar patterns of temperature. Additionally, a modified susceptible-exposed-infectious-recovered (M-SEIR) model, with adjustment for climatic factors, was used to provide a complete characterization of the impact of climate on the dynamics of the COVID-19 epidemic.

OBJECTIVETo investigate the impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak.DESIGNEcological study.SETTING31 provincial-level regions in mainland China.MAIN OUTCOME MEASURESData on COVID-19 incidence and climate between Jan 20 and Feb 29, 2020.RESULTSThe number of new confirm COVID-19 cases in mainland China peaked on Feb 1, 2020. COVID-19 daily incidence were lowest at -10 °C and highest at 10 °C, while the maximum incidence was observed at the absolute humidity of approximately 7 g/m3. COVID-19 incidence changed with temperature as daily incidence decreased when the temperature rose. No significant association between COVID-19 incidence and absolute humidity was observed in distributed lag nonlinear models. Additionally, A modified susceptible-exposed-infectious-recovered (M-SEIR) model confirmed that transmission rate decreased with the increase of temperature, leading to further decrease of infection rate and outbreak scale.CONCLUSIONTemperature is an environmental driver of the COVID-19 outbreak in China. Lower and higher temperatures might be positive to decrease the COVID-19 incidence. M-SEIR models help to better evaluate environmental and social impacts on COVID-19.What is already known on this topicMany infectious diseases present an environmental pattern in their incidence.Environmental factors, such as climate and weather condition, could drive the space and time correlations of infectious diseases, including influenza.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be transmitted through aerosols, large droplets, or direct contact with secretions (or fomites) as influenza virus can.Little is known about environmental pattern in COVID-19 incidence.What this study addsThe significant association between COVID-19 daily incidence and temperature was confirmed, using 3 methods, based on the data on COVID-19 and weather from 31 provincial-level regions in mainland China.Environmental factors were considered on the basis of SEIR model, and a modified susceptible-exposed-infectious-recovered (M-SEIR) model was developed.Simulations of the COVID-19 outbreak in Wuhan presented similar effects of temperature on incidence as the incidence decrease with the increase of temperature.

The outbreak of the coronavirus disease 2019 was first reported in Wuhan in December 2019 and gradually spread to other areas in China. After implementation of prevention and control measures, the estimation of the epidemic trend is needed. A phase- and region-adjusted SEIR model was applied for modeling and predicting the number of cases in Wuhan, Hubei Province and regions outside Hubei Province in China. The estimated number of infections could reach its peak in late February 2020 in Wuhan and Hubei Province, which is 55 303-84 520 and 83 944-129 312, respectively, while the epidemic peaks in regions outside Hubei Province in China could appear on February 13, 2020 with the estimated 13 035-19 108 cases. According to the estimation, the outbreak would abate in March and April all over China. Current estimation provided evidence for planned work resumption under stringent prevention and control in China to further support the fight against the epidemic. Nevertheless, there is still possibility of the second outbreak brought by the work resumption and population migration, especially from Hubei Province and high intensity cities outside Hubei Province. Strict prevention and control measures still need to be considered in the regions with high intensity of epidemic and densely-populated cities.

The world must act fast to contain wider international spread of the epidemic of COVID-19 now. The unprecedented public health efforts in China have contained the spread of this new virus. Measures taken in China are currently proven to reduce human-to-human transmission successfully. We summarized the effective intervention and prevention measures in the fields of public health response, clinical management, and research development in China, which may provide vital lessons for the global response. It is really important to take collaborative actions now to save more lives from the pandemic of COVID-19.

Background: An outbreak of clusters of viral pneumonia due to 2019 novel Coronavirus (2019-nCoV) happened in Wuhan, Hubeprovince in China in December 2019 Si

Background: The outbreak of the Coronavirus Disease 2019 (COVID-19) was first reported in Wuhan in December 2019 and gradually spread to other areas in Hubei Province and regions outside Hubei Province in China. After implementation of prevention and control measures in China, the estimation of the epidemic trend is needed so as to predict the possible infection scale and epidemic characteristics of COVID-19, furtherly, to provide potential evaluation criteria for the effectiveness of current prevention and control measures and, to offer theoretical support for current decision-making on infectious disease prevention. Methods: A phase- and region-adjusted SEIR (Susceptible, Exposed, Infectious and Removed) model was applied for modeling and predicting the number of COVID-19 cases in Wuhan, Hubei Province and regions outside Hubei Province in China. Findings: The estimated number of infections would reach the peak in late February 2020 in Wuhan and Hubei Province, which is 55,303–84,520 and 83,944–129,312, respectively. The epidemic peaks in regions outside Hubei Province in China appeared on 13th Feb with the estimated 13,035–19,108 confirmed cases. Wuhan had an infection rate of more than 3,900 per million population as of 18th Feb 2020. In the areas adjacent to Wuhan, the average infection rate was around 550 per million population. In major financial regions (Beijing, Shanghai, and Guangdong) and provinces neighboring Hubei Province, the infection rate was over 10 per million population. Interpretation: It is reasonable to speculate that the outbreak would be braked soon with the very strong prevention measures taken all over China. Current estimation provided evidence for planned work resumption under stringent prevention and control in regions outside Hubei Province in China to further support the fight against the epidemic. Nevertheless, strict prevention and control measures should still be maintained, especially in the regions with high intensity of epidemic such as Hunan, Guangzhou, Zhejiang Province as well as densely-populated cities such as Shanghai and Beijing, considering the possibility of the second outbreak brought by the work resumption and population migration, especially from Hubei Province and high-intensity cities outside Hubei Province. Funding Statement: This work is funded by Medicine and Engineering Interdisciplinary Research Fund of Shanghai Jiao Tong University (YG2020YQ06), the National Key Research and Development Project (2018YFC1705100, 2018YFC1705103) and the National Natural Science Foundation of China (71673187). Declaration of Interests: All authors declare no competing interests.

Arsenic is the leading toxicant of hazardous environmental chemicals, which is linked with neurotoxicity including cognitive dysfunction, neurodevelopmental alterations and neurodegenerative disorders. It has been suggested that sustained pro-inflammatory response is one of the triggering factors of arsenic-induced neurotoxicity. Microglia, the immune cells in the central nervous system, response to physiological and pathological stress, and release a large array of pro-inflammatory cytokines if activated excessively. Several studies indicated that arsenic was capable of inducing microglia activation, however, the role of the subsequently released pro-inflammatory cytokines in arsenic-induced neurotoxicity remains to be elucidated. Our findings demonstrated that arsenic-induced cognitive dysfunction, microglia activation, up-regulation and release of IL-1β and ER stress-mediated apoptosis could be attenuated by minocycline, a recognized inhibitor of microglia activation. In addition, the IL-1 receptor antagonist IL-1ra diminished arsenic-induced activation of ER stress-mediated apoptotic pathway PERK/eIF2α/ATF4/CHOP and neuronal apoptosis. Our findings provided evidences that arsenic-induced microglia activation also contributed to neuronal apoptosis through pro-inflammatory cytokine. Microglia-derived IL-1β promoted hippocampal neuronal apoptosis through ER stress-mediated PERK/eIF2α/ATF4/CHOP apoptotic pathway. Neuronal apoptosis induced by prolonged activation of microglia was partially involved in the arsenic-induced cognitive dysfunction.