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1.
Zhongguo Meijie Shengwuxue ji Kongzhi Zazhi = Chinese Journal of Vector Biology and Control ; 31(6):633-638, 2020.
Article in Chinese | CAB Abstracts | ID: covidwho-1726807

ABSTRACT

Vectors can transmit Viruses by mechanical transmission. and some species can transmit Viruses by biological means. As for whether vectors can transmit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-Z), this article assess the risk of several categories of vectors closely related to humans in transmitting SARS-CoV-Z. 50 as to provide a basis for developing more precise SARS-CoV-Z prevention and control measures. Based on the collected literature. the risk matrix method was used to evaluate the probability of vectors transmitting SARS-CoV-Z and determine the level of exposure to SARS-CoV-Z and the risk level of SARS-CnV-Z transmission for different vectors in different places. The preliminary results showed that the risk of mosquitoes in transmitting SARS-CUV-Z was Very low: rodents. cockroaches. and flies had a potential risk of mechanical transmission of SARS-COV-Z, and rodents also had a potential risk of biological transmission of SARS-CoV-Z;the three categories of vectors had different risks of the transmission of SARS-CoV-Z in different places, and the risk level of transmission was associated with the risk level of exposure to SARS-CoV-Z, as well as the distribution, density, and activity of vectors. In genera]. the risk of mosquitoes in transmitting SARS-CoV-Z could he excluded: the vectors including rodents, cockroaches, and flies have a potential but low risk of transmitting SARS-CoV-Z in specific planes and surroundings exposed to SARS-COV-Z.

3.
Transactions in GIS : TG ; 2021.
Article in English | EuropePMC | ID: covidwho-1564272

ABSTRACT

The second COVID‐19 outbreak in Beijing was controlled by non‐pharmaceutical interventions, which avoided a second pandemic. Until mass vaccination achieves herd immunity, cities are at risk of similar outbreaks. It is vital to quantify and simulate Beijing's non‐pharmaceutical interventions to find effective intervention policies for the second outbreak. Few models have achieved accurate intra‐city spatio‐temporal epidemic spread simulation, and most modeling studies focused on the initial pandemic. We built a dynamic module of infected case movement within the city, and established an urban spatially epidemic simulation model (USESM), using mobile phone signaling data to create scenarios to assess the impact of interventions. We found that: (1) USESM simulated the transmission process of the epidemic within Beijing;(2) USESM showed the epidemic curve and presented the spatial distribution of epidemic spread on a map;and (3) to balance resources, interventions, and economic development, nucleic acid testing intensity could be increased and restrictions on human mobility in non‐epidemic areas eased.

5.
Innovation (Camb) ; 2(3): 100139, 2021 Aug 28.
Article in English | MEDLINE | ID: covidwho-1275767

ABSTRACT

The evidence for the effects of environmental factors on COVID-19 case fatality remains controversial, and it is crucial to understand the role of preventable environmental factors in driving COVID-19 fatality. We thus conducted a nationwide cohort study to estimate the effects of environmental factors (temperature, particulate matter [PM2.5, PM10], sulfur dioxide [SO2], nitrogen dioxide [NO2], and ozone [O3]) on COVID-19 case fatality. A total of 71,808 confirmed COVID-19 cases were identified and followed up for their vital status through April 25, 2020. Exposures to ambient air pollution and temperature were estimated by linking the city- and county-level monitoring data to the residential community of each participant. For each participant, two windows were defined: the period from symptom onset to diagnosis (exposure window I) and the period from diagnosis date to date of death/recovery or end of the study period (exposure window II). Cox proportional hazards models were used to estimate the associations between these environmental factors and COVID-19 case fatality. COVID-19 case fatality increased in association with environmental factors for the two exposure windows. For example, each 10 µg/m3 increase in PM2.5, PM10, O3, and NO2 in window I was associated with a hazard ratio of 1.11 (95% CI 1.09, 1.13), 1.10 (95% CI 1.08, 1.13), 1.09 (95 CI 1.03, 1.14), and 1.27 (95% CI 1.19, 1.35) for COVID-19 fatality, respectively. A significant effect was also observed for low temperature, with a hazard ratio of 1.03 (95% CI 1.01, 1.04) for COVID-19 case fatality per 1°C decrease. Subgroup analysis indicated that these effects were stronger in the elderly, as well as in those with mild symptoms and living in Wuhan or Hubei. Overall, the sensitivity analyses also yielded consistent estimates. Short-term exposure to ambient air pollution and low temperature during the illness would play a nonnegligible part in causing case fatality due to COVID-19. Reduced exposures to high concentrations of PM2.5, PM10, O3, SO2, and NO2 and low temperature would help improve the prognosis and reduce public health burden.

6.
Clin Infect Dis ; 71(16): 2045-2051, 2020 11 19.
Article in English | MEDLINE | ID: covidwho-1153144

ABSTRACT

BACKGROUND: The unprecedented outbreak of corona virus disease 2019 (COVID-19) infection in Wuhan City has caused global concern; the outflow of the population from Wuhan was believed to be a main reason for the rapid and large-scale spread of the disease, so the government implemented a city-closure measure to prevent its transmission considering the large amount of travel before the Chinese New Year. METHODS: Based on the daily reported new cases and the population-movement data between 1 and 31 January, we examined the effects of population outflow from Wuhan on the geographical expansion of the infection in other provinces and cities of China, as well as the impacts of the city closure in Wuhan using different closing-date scenarios. RESULTS: We observed a significantly positive association between population movement and the number of the COVID-19 cases. The spatial distribution of cases per unit of outflow population indicated that the infection in some areas with a large outflow of population might have been underestimated, such as Henan and Hunan provinces. Further analysis revealed that if the city-closure policy had been implemented 2 days earlier, 1420 (95% confidence interval, 1059-1833) cases could have been prevented, and if 2 days later, 1462 (1090-1886) more cases would have been possible. CONCLUSIONS: Our findings suggest that population movement might be one important trigger for the transmission of COVID-19 infection in China, and the policy of city closure is effective in controlling the epidemic.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , China/epidemiology , Cities/epidemiology , Confidence Intervals , Humans , Pandemics
7.
Environ Health Perspect ; 128(11): 115001, 2020 11.
Article in English | MEDLINE | ID: covidwho-1054874

ABSTRACT

BACKGROUND: Modeling suggests that climate change mitigation actions can have substantial human health benefits that accrue quickly and locally. Documenting the benefits can help drive more ambitious and health-protective climate change mitigation actions; however, documenting the adverse health effects can help to avoid them. Estimating the health effects of mitigation (HEM) actions can help policy makers prioritize investments based not only on mitigation potential but also on expected health benefits. To date, however, the wide range of incompatible approaches taken to developing and reporting HEM estimates has limited their comparability and usefulness to policymakers. OBJECTIVE: The objective of this effort was to generate guidance for modeling studies on scoping, estimating, and reporting population health effects from climate change mitigation actions. METHODS: An expert panel of HEM researchers was recruited to participate in developing guidance for conducting HEM studies. The primary literature and a synthesis of HEM studies were provided to the panel. Panel members then participated in a modified Delphi exercise to identify areas of consensus regarding HEM estimation. Finally, the panel met to review and discuss consensus findings, resolve remaining differences, and generate guidance regarding conducting HEM studies. RESULTS: The panel generated a checklist of recommendations regarding stakeholder engagement: HEM modeling, including model structure, scope and scale, demographics, time horizons, counterfactuals, health response functions, and metrics; parameterization and reporting; approaches to uncertainty and sensitivity analysis; accounting for policy uptake; and discounting. DISCUSSION: This checklist provides guidance for conducting and reporting HEM estimates to make them more comparable and useful for policymakers. Harmonization of HEM estimates has the potential to lead to advances in and improved synthesis of policy-relevant research that can inform evidence-based decision making and practice. https://doi.org/10.1289/EHP6745.


Subject(s)
Air Pollution , COVID-19 , Coronavirus , Severe Acute Respiratory Syndrome , Climate Change , Disease Outbreaks , Epidemiologic Studies , Humans , SARS-CoV-2
8.
IEEE Access ; 8: 216752-216761, 2020.
Article in English | MEDLINE | ID: covidwho-1003891

ABSTRACT

The first wave of the 2019 novel coronavirus (COVID-19) epidemic in China showed there was a lag between the reduction in human mobility and the decline in COVID-19 transmission and this lag was different in cities. A prolonged lag would cause public panic and reflect the inefficiency of control measures. This study aims to quantify this time-lag effect and reveal its influencing socio-demographic and environmental factors, which is helpful to policymaking in controlling COVID-19 and other potential infectious diseases in the future. We combined city-level mobility index and new case time series for 80 most affected cities in China from Jan 17 to Feb 29, 2020. Cross correlation analysis and spatial autoregressive model were used to estimate the lag length and determine influencing factors behind it, respectively. The results show that mobility is strongly correlated with COVID-19 transmission in most cities with lags of 10 days (interquartile range 8 - 11 days) and correlation coefficients of 0.68 ± 0.12. This time-lag is consistent with the incubation period plus time for reporting. Cities with a shorter lag appear to have a shorter epidemic duration. This lag is shorter in cities with larger volume of population flow from Wuhan, higher designated hospitals density and urban road density while economically advantaged cities tend to have longer time lags. These findings suggest that cities with compact urban structure should strictly adhere to human mobility restrictions, while economically prosperous cities should also strengthen other non-pharmaceutical interventions to control the spread of the virus.

9.
Proc Natl Acad Sci U S A ; 117(42): 26151-26157, 2020 10 20.
Article in English | MEDLINE | ID: covidwho-807983

ABSTRACT

Emerging evidence suggests a resurgence of COVID-19 in the coming years. It is thus critical to optimize emergency response planning from a broad, integrated perspective. We developed a mathematical model incorporating climate-driven variation in community transmissions and movement-modulated spatial diffusions of COVID-19 into various intervention scenarios. We find that an intensive 8-wk intervention targeting the reduction of local transmissibility and international travel is efficient and effective. Practically, we suggest a tiered implementation of this strategy where interventions are first implemented at locations in what we call the Global Intervention Hub, followed by timely interventions in secondary high-risk locations. We argue that thinking globally, categorizing locations in a hub-and-spoke intervention network, and acting locally, applying interventions at high-risk areas, is a functional strategy to avert the tremendous burden that would otherwise be placed on public health and society.


Subject(s)
Communicable Disease Control/methods , Communicable Diseases, Emerging/prevention & control , Coronavirus Infections/prevention & control , Disease Transmission, Infectious/prevention & control , Global Health/trends , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Betacoronavirus , COVID-19 , Climate , Communicable Diseases, Emerging/epidemiology , Communicable Diseases, Emerging/transmission , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Forecasting , Humans , International Cooperation , Models, Theoretical , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , SARS-CoV-2 , Travel
10.
SSRN; 2020.
Preprint | SSRN | ID: ppcovidwho-492

ABSTRACT

Objective: To explore the epidemic dynamics of the pneumonia caused by 2019-nCoV in China. br br Methods: A descriptive analysis was utilized to explore the

11.
Hum Vaccin Immunother ; 16(7): 1668-1674, 2020 07 02.
Article in English | MEDLINE | ID: covidwho-133495

ABSTRACT

PURPOSE: To estimate influenza-associated excess mortality rates (EMRs) in Chongqing from 2012 to 2018. METHODS: We obtained weekly mortality data for all-cause and four underlying causes of death (circulatory and respiratory disease (CRD), pneumonia and influenza (P&I), chronic obstructive pulmonary disease (COPD) and ischemic heart disease (IDH)), and influenza surveillance data, from 2012 to 2018. A negative-binomial regression model was used to estimate influenza-associated EMRs in two age groups (<65 years and ≥65 years). RESULTS: It was estimated that an annual average of 10025 influenza-associated deaths occurred in Chongqing, corresponding to 5.2% of all deaths. The average EMR for all-cause death associated with influenza was 33.5 (95% confidence interval (CI): 31.5-35.6) per 100 000 persons, and in separate cause-specific models we attributed 24.7 (95% CI: 23.3-26.0), 0.8 (95% CI: 0.7-0.8), 8.5 (95% CI: 8.1-9.0) and 5.0 (95% CI: 4.7-5.3) per 100 000 persons EMRs to CRD, P&I, COPD and IDH, respectively. The estimated EMR for influenza B virus was 20.6 (95% CI: 20.3-21.0), which was significantly higher than the rates of 5.3 (95% CI: 4.5-6.1) and 7.5 (95% CI: 6.7-8.3) for A(H3N2) and A(H1N1) pdm09 virus, respectively. The estimated EMR was 152.3 (95% CI: 136.1-168.4) for people aged ≥65 years, which was significantly higher than the rate for those aged <65 years (6.8, 95% CI: 6.3-7.2). CONCLUSIONS: Influenza was associated with substantial EMRs in Chongqing, especially among elderly people. Influenza B virus caused a relatively higher excess mortality impact compared with A(H1N1)pdm09 and A(H3N2). It is advisable to optimize future seasonal influenza vaccine reimbursement policy in Chongqing to curb disease burden.


Subject(s)
Influenza A Virus, H1N1 Subtype , Influenza Vaccines , Influenza, Human , Aged , China/epidemiology , Humans , Influenza A Virus, H3N2 Subtype , Influenza, Human/epidemiology , Seasons
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