The impact of temperature on the transmission potential and virulence of COVID-19 in Tokyo, Japan

AbstractAssessing the impact of temperature on COVID-19 epidemiology is critical for implementing non-pharmaceutical interventions. However, few studies have accounted for the nature of contagious diseases, i.e., their dependent happenings. We aimed to quantify the impact of temperature on the transmissibility and virulence of COVID-19 in Tokyo, Japan, employing two epidemiological measurements of transmissibility and severity: the effective reproduction number ($$R_{t}$$ R t ) and case fatality risk (CFR). We estimated the $$R_{t}$$ R t and time-delay adjusted CFR and to subsequently assess the nonlinear and delayed effect of temperature on $$R_{t}$$ R t and time-delay adjusted CFR. For $$R_{t}$$ R t at low temperatures, the cumulative relative risk (RR) at the first temperature percentile (3.3 °C) was 1.3 (95% confidence interval (CI): 1.1–1.7). As for the virulence to humans, moderate cold temperatures were associated with higher CFR, and CFR also increased as the temperature rose. The cumulative RR at the 10th and 99th percentiles of temperature (5.8 °C and 30.8 °C) for CFR were 3.5 (95% CI: 1.3–10.0) and 6.4 (95% CI: 4.1–10.1). Our results suggest the importance to take precautions to avoid infection in both cold and warm seasons to avoid severe cases of COVID-19. The results and our proposed approach will also help in assessing the possible seasonal course of COVID-19 in the future.

Download Full-text

Effect of extreme cold temperatures on quasi-static indentation and impact behavior of woven carbon fiber epoxy composite sandwich panels with nomex honeycomb core

Journal of Sandwich Structures & Materials ◽

10.1177/1099636221993876 ◽

2021 ◽

pp. 109963622199387

Author(s):

Mathilde Jean-St-Laurent ◽

Marie-Laure Dano ◽

Marie-Josée Potvin

Keyword(s):

Epoxy Composite ◽

Impact Behavior ◽

Effect Of Temperature ◽

Cold Temperatures ◽

Composite Sandwich Panels ◽

Composite Sandwich ◽

Static Indentation ◽

Nomex Honeycomb ◽

Extreme Cold ◽

The Impact

The effect of extreme cold temperatures on the quasi-static indentation and the low velocity impact behavior of woven carbon/epoxy composite sandwich panels with Nomex honeycomb core was investigated. Impact tests were performed at room temperature, –70°C, and –150°C. Two sizes of hemispherical impactor were used combined to three different impactor masses. All the impact tests were performed at the same initial impact velocity. The effect of temperature on the impact behavior is investigated by studying the load history, load-displacement curves and transmitted energy as a function of time curves. Impact damage induced at various temperatures was studied using different non-destructive and destructive techniques. Globally, more damages are induced with impact temperature decreasing. The results also show that the effect of temperature on the impact behavior is function of the impactor size.

Download Full-text

The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China

Respiratory Research ◽

10.1186/s12931-021-01744-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Rui Zhang ◽

Yujie Meng ◽

Hejia Song ◽

Ran Niu ◽

Yu Wang ◽

...

Keyword(s):

Air Pollution ◽

High Temperature ◽

Air Pollutants ◽

Meteorological Data ◽

Effect Of Temperature ◽

Distributed Lag ◽

Modification Effect ◽

Incidence Risk ◽

The Impact ◽

The Relationship

Abstract Background Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Methods The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. Results Overall, a 10 μg/m3 increment of O3, PM2.5, PM10 and NO2 could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution. Conclusion Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.

Download Full-text

The role of age distribution, time lag between reporting and death and healthcare system capacity on case fatality estimates of COVID-19

10.1101/2020.05.16.20104117 ◽

2020 ◽

Cited By ~ 1

Author(s):

Patrizio Vanella ◽

Christian Wiessner ◽

Anja Holz ◽

Gerard Krause ◽

Annika Moehl ◽

...

Keyword(s):

Time Lag ◽

Age Distribution ◽

Case Fatality ◽

System Capacity ◽

Time Lags ◽

Cross Country ◽

The Difference ◽

Case Fatality Risk ◽

The Impact

European countries report large differences in coronavirus disease (COVID-19) case fatality risk (CFR). CFR estimates depend on demographic characteristics of the cases, time lags between reporting of infections and deaths and infrastructural characteristics, such as healthcare and surveillance capacities. We discuss the impact of these factors on the CFR estimates for Germany, Italy, France, and Spain for the COVID-19 pandemic from early March to mid-April, 2020. We found that, first, a large proportion of the difference in CFRs can be attributed to different age structures of the cases. Second, lags of 5-10 days between day of case report and death should be used, since these provide the most constant estimates. Third, for France, Italy, and Spain, intensive care beds occupied by COVID-19 patients were positively associated with fatality risks of hospitalized cases. Our results highlight that cross-country comparisons of crude CFR estimates can be misleading and should be avoided.

Download Full-text

Evaluating the impact of the weather conditions on the influenza propagation

10.21203/rs.2.10422/v3 ◽

2020 ◽

Author(s):

David E. Singh ◽

maria-cristina marinescu ◽

Jesus Carretero ◽

Concepcion Delgado-Sanz ◽

Diana Gomez-Barroso ◽

...

Keyword(s):

Relative Humidity ◽

Meteorological Data ◽

Personal Characteristics ◽

Effect Of Temperature ◽

Urban Region ◽

Influenza Surveillance ◽

Interaction Patterns ◽

Meteorological Model ◽

Simulation Results ◽

The Impact

Abstract Background: Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph's modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). Methods: Our meteorological model is based on the regression model developed by Barreca and Shimshack, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region. Results: We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10\% produces an increment of about 1.6\% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1\% per extra degree. Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.

Download Full-text

Estimating the Risk of COVID-19 Death during the Course of the Outbreak in Korea, February–May 2020

Journal of Clinical Medicine ◽

10.3390/jcm9061641 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1641 ◽

Cited By ~ 9

Author(s):

Eunha Shim ◽

Kenji Mizumoto ◽

Wongyeong Choi ◽

Gerardo Chowell

Keyword(s):

Time Delay ◽

Early Stage ◽

Case Fatality ◽

Credible Interval ◽

Adjusted Risk ◽

Risk Of Death ◽

Early Implementation ◽

Daily Series ◽

Case Fatality Risk ◽

Reporting Delay

Background: In Korea, a total of 10,840 confirmed cases of COVID-19 including 256 deaths have been recorded as of May 9, 2020. The time-delay adjusted case fatality risk (CFR) of COVID-19 in Korea is yet to be estimated. Methods: We obtained the daily series of confirmed cases and deaths in Korea reported prior to May 9, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in Daegu, Gyeongsangbuk-do, other regions in Korea, as well as the entire country. Results: Our model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do; this was partially due to the reporting delay. Our estimates of the risk of death in Gyeongsangbuk-do reached 25.9% (95% Credible Interval (CrI): 19.6%–33.6%), 20.8% (95% CrI: 18.1%–24.0%) in Daegu, and 1.7% (95% CrI: 1.1%–2.5%) in other regions, whereas the national estimate was 10.2% (95% CrI: 9.0%–11.5%). Conclusions: The latest estimates of CFR of COVID-19 in Korea are considerably high, even with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings. Geographic differences in the CFR are likely influenced by clusters tied to hospitals and nursing homes.

Download Full-text

Estimating the risk of COVID-19 death during the course of the outbreak in Korea, February-March, 2020

10.1101/2020.03.30.20048264 ◽

2020 ◽

Cited By ~ 4

Author(s):

Eunha Shim ◽

Kenji Mizumoto ◽

Wongyeong Choi ◽

Gerardo Chowell

Keyword(s):

Time Delay ◽

Early Stage ◽

Case Fatality ◽

Adjusted Risk ◽

Early Implementation ◽

Daily Series ◽

Geographic Differences ◽

Case Fatality Risk ◽

The City ◽

Reporting Delay

AbstractBackgroundIn Korea, a total of 8,799 confirmed cases of COVID-19 including 102 deaths have been recorded as of Mar 21, 2020. The time-delay adjusted case fatality risk of COVID-19 in Korea is yet to be estimated.MethodsWe obtained the daily series of confirmed cases and deaths in Korea reported prior to March 21, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in the city of Daegu, Gyeongsangbuk-do, other regions in Korea, as well as for the entire country.ResultsOur model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do, partially due to the reporting delay. Our estimates of the risk for death in Gyeongsangbuk-do reached 2.4% (95% CrI: 1.6-3.4%), 1.3% (95% CrI: 1.0-1.6%) in Daegu and 0.7% (95% CrI: 0.3-1.4%) in other regions, whereas the national estimate of the risk for death was estimated at 1.4% (95% CrI: 1.2-1.7%) in Korea.ConclusionsThe relatively low CFRs are associated with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings in Korea. Geographic differences in CFR are likely influenced by clusters of nosocomial transmission.

Download Full-text

Evaluating the impact of the weather conditions on the influenza propagation

10.21203/rs.2.10422/v1 ◽

2019 ◽

Author(s):

David E. Singh ◽

Maria-cristina Marinescu ◽

Jesus Carretero ◽

Concepcion Delgado-Sanz ◽

Diana Gomez-Barroso ◽

...

Keyword(s):

Relative Humidity ◽

Meteorological Data ◽

Personal Characteristics ◽

Effect Of Temperature ◽

Urban Region ◽

Influenza Surveillance ◽

Interaction Patterns ◽

Meteorological Model ◽

Simulation Results ◽

The Impact

Abstract Background: Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph's modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET). Methods: Our meteorological model is based on the regression model developed by Barreca and Shimshack, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region. Results: We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10\% produces an increment of about 1.6\% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1\% per extra degree. Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.

Download Full-text

Effect of evidence updates on key determinants of measles vaccination impact: a DynaMICE modelling study in ten high-burden countries

10.1101/2021.05.28.21257964 ◽

2021 ◽

Author(s):

Han Fu ◽

Kaja Abbas ◽

Petra Klepac ◽

Kevin van Zandvoort ◽

Hira Tanvir ◽

...

Keyword(s):

Vaccine Efficacy ◽

Reproduction Number ◽

Case Fatality ◽

Measles Vaccine ◽

Disability Adjusted Life Years ◽

High Burden ◽

Disability Adjusted Life ◽

Life Years ◽

Case Fatality Risk ◽

The Impact

Background Model-based estimates of measles burden and the impact of measles-containing vaccine (MCV) are crucial for global health priority setting. Recently, evidence from systematic reviews and database analyses have improved our understanding of key determinants of measles vaccine impact. We explore how updated representations of these determinants affect model-based estimation of MCV impact in ten countries with highest measles burden. Methods Using Dynamic Measles Immunisation Calculation Engine (DynaMICE), an age-structured compartmental model of measles transmission and vaccination, we evaluated the effect of evidence updates for five determinants of MCV impact: case fatality risk, contact patterns, age-dependent vaccine efficacy, the potential of supplementary immunisation activities (SIAs) to reach zero-dose children, and the basic reproduction number. We also evaluated the incremental impact of the first dose (MCV1), second dose (MCV2), and SIA dose of measles vaccines, based on country-specific coverage estimates from the World Health Organization. The MCV impact was assessed by cumulative vaccine-averted cases, deaths, and disability-adjusted life years over 2000-2050. Results Incorporated with the updated data sources, DynaMICE projected 252 million measles cases, 3.7 million deaths and 230 million disability-adjusted life years incurred over 2000-2050 in the ten high-burden countries when MCV1, MCV2, and SIA doses were implemented. Compared to no vaccination, the administration of MCV1 contributed to 66% reduction in cumulative measles burden, while MCV2 and SIAs reduced this further to 89%. With routine and supplementary vaccination, India and countries with high vaccination coverage could maintain measles incidence below 1 per million. Among the updated determinants, shifting from fixed to linearly-varying vaccine efficacy by age and from static to time-varying case fatality risks had the biggest effect on the model projections of MCV impact. While varying the basic reproduction number showed a limited effect on vaccine-averted burden, updates on the other four determinants together led to an overall reduction of MCV impact by 0.87-26.7%. Conclusions High coverage of measles vaccine through both routine and SIA delivery platforms are essential for achieving and maintaining low incidence in high-measles burden settings. Incorporating updated evidence particularly on vaccine efficacy and case fatality risk reduces estimates of the impact of vaccination slightly, but its overall impact remains considerable.

Download Full-text

Effect of ambient temperature on outpatient admission for osteoarthritis and rheumatoid arthritis in a subtropical Chinese city

10.21203/rs.3.rs-34830/v1 ◽

2020 ◽

Author(s):

Desheng Zhao ◽

Jian Cheng ◽

Ping Bao ◽

Yanwu Zhang ◽

Fengjuan Liang ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Ambient Temperature ◽

Meteorological Data ◽

Weather Conditions ◽

Individual Characteristics ◽

Effect Of Temperature ◽

Temperature Decrease ◽

Distributed Lag ◽

The Impact ◽

The Relationship

Abstract Background Current findings on the impact of weather conditions on osteoarthritis (OA) and rheumatoid arthritis (RA) are sparse and not conclusive. This study aimed to investigate the relationship between temperature change and OA/RA admission. Methods Daily OA/RA admission and meteorological data from 1 January 2014 to 31 December 2017 in Hefei, China, were collected. We quantified the relationship between ambient temperature and OA/RA admission using a distributed lag nonlinear model (DLNM). The effect modifications by gender and age were also examined. Results Sudden temperature decrease was significantly associated with RA admission (25th percentile of temperature versus 50th percentile of temperature), with the acute and largest effect at current days lag (RR: 1.063, 95%CI: 1.010–1.118). However, no association between temperature and OA admission was observed. When conducting subgroup analyses by individual characteristics, we found that females and patients aged 41–65 years were more vulnerable to temperature decrease than males, patients aged 0–40 and ≧ 66 years, respectively. Conclusions This study suggested that sudden temperature decrease was a risk factor for increase RA admission. Females and patients aged 41–65 years were particularly vulnerable to the effect of temperature decrease.

Download Full-text