Dataset Information

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

ABSTRACT: Assessing 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 ([Formula: see text]) and case fatality risk (CFR). We estimated the [Formula: see text] and time-delay adjusted CFR and to subsequently assess the nonlinear and delayed effect of temperature on [Formula: see text] and time-delay adjusted CFR. For [Formula: see text] 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.

SUBMITTER: Yamasaki L

PROVIDER: S-EPMC8716537 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The 2019-Coronavirus (COVID-19) pandemic has had a global impact. The effect of environmental temperature on transmissibility and fatality rate of COVID-19 and protective efficacy of Bacillus Calmette-Guérin (BCG) vaccination towards COVID-19 remains ambiguous. Therefore, we explored the global impact of environmental temperature and neonatal BCG vaccination coverage on transmissibility and fatality rate of COVID-19. The COVID-19 data for reported cases, deaths and global temperature were collected from 31st December 2020 to 3rd April 2020 for 67 countries. Temperature data were split into quartiles for all three categories (minimum temperature, maximum temperature and mean temperature). The impact of three types of temperature data and policy of BCG vaccination on COVID-19 infection was determined by applying the multivariable two-level negative binomial regression analysis keeping daily new cases and daily mortality as outcome. The highest number of cases fell in the temperature categories as following: mean temperature in the second quartile (6°C to 10.5°C), median 26, interquartile range (IQR) 237; minimum temperature in the first quartile (-26°C to 1°C), median 23, IQR 173; maximum temperature in the second quartile (10°C to 16°C), median 27.5, IQR 219. For the minimum temperature category, 28% statistically significant lower incidence was noted for new cases from the countries falling in the second quartile (2°C to 6°C) compared with countries falling in the first quartile (-26°C to 1°C) (incidence rate ratio [IRR] 0.72, 95% confidence interval [CI] 0.57 to 0.93). However, no statistically significant difference in incidence rate was observed for mean temperature categories in comparison to the first quartile. Countries with BCG vaccination policy had 58% less mortality as compared with countries without BCG coverage (IRR 0.42; 95% CI 0.18 to 0.95). Our exploratory study provides evidence that high temperature might not be associated with low transmissibility and countries having neonatal BCG vaccination policy had a low fatality rate of COVID-19.

Project description:BACKGROUND:The number of cases of novel coronavirus disease 2019 (COVID-19) in Japan have risen since the first case was reported on January 24, 2020, and 6225 infections have been reported as of June 30, 2020. On April 8, 2020, our hospital began screening patients via pre-admission reverse transcriptase-polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and chest computed tomography (CT). Although no patients exhibited apparent pneumonia, treatment delay or changes in treatment plans were required for a few patients based on the results of screening tests. During an emerging infectious disease pandemic, the likelihood of being infected, as well as the disease itself, affects clinical decision making in several ways. We summarized and presented our experience. CASE PRESENTATION:After the introduction of pre-admission screening, RT-PCR and CT were performed in 200 and 76 patients, respectively, as of June 30, 2020. The treatment of five patients, including two patients with cervical cancer, two patients with ovarian tumors, and one patient with ovarian cancer, was affected by the results. Two asymptomatic RT-PCR-positive patients did not develop COVID-19, but their treatment was delayed until the confirmation of negative results. The other three patients were RT-PCR-negative, but abnormal CT findings suggested the possibility of COVID-19, which delayed treatment. The patients receiving first-line preoperative chemotherapy for ovarian cancer had clinically evident exacerbations because of the treatment delay. CONCLUSION:During the epidemic phase of an emerging infectious disease, we found that COVID-19 has several other effects besides its incidence. The postponing treatment was the most common, therefore, treatment of ovarian tumors and ovarian cancer was considered to be the most likely to be affected among gynecological diseases. Protocols that allow for easy over-diagnosis can be disadvantageous, mainly because of treatment delays, and therefore, the protocols must be developed in light of the local infection situation.

Project description:BackgroundCOVID-19 currently poses a global public health threat. Although Tokyo, Japan, is no exception to this, it was initially affected by only a small-level epidemic. Nevertheless, medical collapse nearly happened since no predictive methods were available to assess infection counts. A standard susceptible-infectious-removed (SIR) epidemiological model has been widely used, but its applicability is limited often to the early phase of an epidemic in the case of a large collective population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies.ObjectiveThis study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count.MethodsTime-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model.ResultsIn a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag T from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of T=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19.ConclusionsA novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets