Seasonal Factors and COVID 19 (COVID-19)

The Effect of Seasonal Factors on the Virulence and Mortality of COVID 19

Corona Virus - 19 Disease (COVID-19), caused by Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2), first appeared in Wuhan city of Hubei province of China and spread rapidly, causing a pandemic. In the period from the first emergence of the disease to the present, 336,790,193 people have been infected and 5,560,718 people have died. Considering the severity of the disease, some quarantine measures should be applied, as in many infectious diseases, among the first measures taken worldwide to prevent the spread of the virus.It has been suggested in a limited number of studies conducted in the last two years that climatic conditions may be among the main factors affecting the spread of SARS-CoV-2. However, there is no consensus on the effects of factors such as temperature, humidity, and wind on the spread of the disease, and there are conflicting results. The aim of this study is to determine the relationship between the rate of spread of COVID-19, the rate of intensive care hospitalization and fatality, and weather conditions.

Study Overview

• Status: Completed
• Conditions: COVID-19 Pandemic
• Intervention / Treatment: Diagnostic test: Polymerase chain reaction

Detailed Description

From the first appearance of COVID-19 to the present, 336,790,193 people have been infected and 5,560,718 people have died (Web Page: https://covid19.who.int/ accessed on 21 January 2022). Considering the severity of the disease, some quarantine measures should be applied, as in many infectious diseases, among the first measures taken worldwide to prevent the spread of the virus. However, these practices may cause the society to be negatively affected economically, socially and psychologically over time (1). Predicting the conditions that increase the spread of the disease, optimizing quarantine practices, preparing health institutions for epidemic disease conditions and minimizing the negative effects of practices on society are very important in such pandemic epidemics that have the potential to affect the whole world for years.

In the last two decades, it has been reported that the survival time of the virus in the air, its spreading rate and virulence are affected by weather conditions such as temperature, humidity, and wind speed in epidemics caused by viruses that are transmitted by droplets and cause respiratory diseases (2,3). Similarly, a limited number of studies conducted in the last two years have suggested that climatic conditions may be among the main factors affecting the spread of SARS-CoV-2 (1.4-6). In addition, almost all of these studies were carried out on the basis of the number of patients presented by the health authorities of the countries, and they are not field studies. This situation, on the other hand, has the potential to ignore patients who have only Polymerase Chain Reaction (PCR) positivity as the basis for patient identification, and therefore patients with clinical and imaging COVID-19 and test negativity. While investigating the relationship between the rate of spread and virulence of COVID-19 and seasonal climatic conditions, conducting the study in a region where all the factors that have the potential to affect the spread of the virus are as similar as possible, over a wide period of time, including seasonal changes, may provide more information. The aim of this study is to determine the relationship between the rate of spread of COVID-19, the rate of intensive care hospitalization and fatality, and weather conditions.

This retrospective observational multicenter study was conducted by examining the 284-day data of Covid-19 outpatient clinics of two large University hospitals located in the south and north of the city of Izmir in Turkey. The province of İzmir, where the research was carried out, is located between 38-39 north latitudes and 26-28 east meridians in terms of geographical location. It is Turkey's 3rd largest city and its total population in 2020 is approximately 4.4 million people. The centers where the study was conducted are the university hospitals in the north and south of the city, which have been serving as pandemic centers since the beginning of the pandemic and where the city's highest number of patient admissions. Before starting the study, approval was obtained from the hospital local ethics committee with the number GOKAE-2020-190 and from the Ministry of Health with the number 2020-05-04T09_17_13.

The data were obtained by examining the patient records who applied to the COVID-19 outpatient clinics of both central hospitals where the study was conducted between March 2020 and January 2021. The data were analyzed day by day, covering 284 days. During the study period, the number, gender, age and clinical outcome of all patients with positive PCR test among the patients who applied to the Covid outpatient clinics of both hospitals (decision of outpatient follow-up, decision of hospitalization, decision of admission to intensive care unit and ex) were recorded. . Patients who were diagnosed with COVID-19 based on clinical and imaging studies and were therefore recommended isolation or hospitalization were enrolled among the patients who had negative PCR tests. While patients with negative PCR were diagnosed with Covid-19 in their applications to the Covid-19 outpatient clinics of both hospitals, the North American Society of Radiology Covid-19 CT imaging expert consensus report is taken as a basis for radiological images. According to this report, those with Lung CT Type 1 or Type 2 are considered Covid-19, while those with Type 3 or who are clinically compatible with Covid-19 in patients with no infiltration on imaging or who were not sent for imaging, accompanied by normal or low white blood cell count and low neutrophil count, have high Patients are diagnosed with Covid-19 using platelet count parameters. The hospital records of these patients were reviewed, and patients whose diagnosis changed according to repeated PCR test, swab test results for other viral agents, repetitive imaging, and laboratory results were excluded from the study. Clinical outcome was followed up 1 month after the first admission of the patients and until discharge or death in hospitalized patients. The application date of the patients who had recurrent applications during the same disease process was taken as the first application date, and the records of the patients whose clinical decision was later changed to hospitalization and intensive care unit were reduced to a single record and the final clinical outcome was taken as the basis. In this way, the number of patients who received outpatient treatment, hospitalized, intensive care and ex-patient treatment due to Covid-19 were determined and recorded. The rate of hospitalization of the patients in the intensive care unit was calculated with the formula of the number of patients admitted to the daily intensive care unit / Number of patients diagnosed with covid daily, and the fatality rate was calculated with the formula: Number of patients who died per day / Number of patients diagnosed with daily covid, and was recorded.

Weather data with official application from the General Directorate of Meteorology, daily minimum temperature (°C), maximum temperature (°C), temperature average (°C), wind speed (m/s) and direction, humidity (%) within the working period. was obtained as the amount of precipitation (kg/m2).

Descriptive statistics; frequency, percentage, mean, standard deviation, median, minimum and maximum values will be obtained. Number and percentage for categorical variables, mean, standard deviation, minimum, maximum values and interquartile range (IQR) for numerical variables will be calculated. Histogram curves, kurtosis-skewness values and the Shapiro-Wilks test will be used to test whether the continuous variables are normally distributed. The seasonal parameters, temperature, humidity, wind and precipitation, will be correlated with the number of patients per day. By obtaining the same parameters and the graph showing the distribution with the number of patients, the temperature, humidity, wind and precipitation levels with the increase in the number of patients will be determined.

• Study Type: Observational
• Enrollment (Actual): 24225

Contacts and Locations

Study Locations

• Turkey
  • İzmir, Turkey, 35360
    • IKCU, Atatürk Eğitim ve Araştırma Hastanesi, Acil Tıp

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

Patients with positive PCR test according to laboratory, imaging and PCR test results from adult patients who applied with Covid-19 symptoms or contact history, and patients diagnosed with COVID-19 as a result of imaging and clinical findings were included in the study. Patients under the age of 18 were excluded from these patients.

Description

Inclusion Criteria:

• Patients with positive PCR test according to laboratory, imaging and PCR test results from adult patients who applied with Covid-19 symptoms or contact history, and patients diagnosed with COVID-19 as a result of imaging and clinical findings were included in the study.

Exclusion Criteria:

• Patients under the age of 18 were excluded from these patients.

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Crossover
• Time Perspectives: Retrospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The rate of spread of SARS-CoV-2	Number of Covid-19 cases per day	284 day

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Intensive care hospitalization rate	Number of Intensive care hospitalized Covid-19 patients	284 day
Covid-19 fatality rate	Number of died patients due Covid-19 daily	284 day

Collaborators and Investigators

• Sponsor: Izmir Katip Celebi University

Publications and helpful links

General Publications

• Scafetta N. Distribution of the SARS-CoV-2 Pandemic and Its Monthly Forecast Based on Seasonal Climate Patterns. Int J Environ Res Public Health. 2020 May 17;17(10):3493. doi: 10.3390/ijerph17103493.
• Shaman J, Kohn M. Absolute humidity modulates influenza survival, transmission, and seasonality. Proc Natl Acad Sci U S A. 2009 Mar 3;106(9):3243-8. doi: 10.1073/pnas.0806852106. Epub 2009 Feb 9.
• Lipsitch M, Viboud C. Influenza seasonality: lifting the fog. Proc Natl Acad Sci U S A. 2009 Mar 10;106(10):3645-6. doi: 10.1073/pnas.0900933106. No abstract available.
• Wu T, Kang S, Peng W, Zuo C, Zhu Y, Pan L, Fu K, You Y, Yang X, Luo X, Jiang L, Deng M. Original Hosts, Clinical Features, Transmission Routes, and Vaccine Development for Coronavirus Disease (COVID-19). Front Med (Lausanne). 2021 Jul 6;8:702066. doi: 10.3389/fmed.2021.702066. eCollection 2021.
• Tosepu R, Gunawan J, Effendy DS, Ahmad OAI, Lestari H, Bahar H, Asfian P. Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Sci Total Environ. 2020 Jul 10;725:138436. doi: 10.1016/j.scitotenv.2020.138436. Epub 2020 Apr 4.
• Ma Y, Zhao Y, Liu J, He X, Wang B, Fu S, Yan J, Niu J, Zhou J, Luo B. Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Sci Total Environ. 2020 Jul 1;724:138226. doi: 10.1016/j.scitotenv.2020.138226. Epub 2020 Mar 26.

Study record dates

Study Major Dates

• Study Start (Actual): January 31, 2021
• Primary Completion (Actual): January 1, 2022
• Study Completion (Actual): March 2, 2022

Study Registration Dates

• First Submitted: March 1, 2022
• First Submitted That Met QC Criteria: March 1, 2022
• First Posted (Actual): March 3, 2022

Study Record Updates

• Last Update Posted (Actual): March 3, 2022
• Last Update Submitted That Met QC Criteria: March 1, 2022
• Last Verified: March 1, 2022

More Information

Terms related to this study

• Keywords: SARS-CoV-2; COVID-19; Seasonal Variation
• Additional Relevant MeSH Terms: Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Virus Diseases; Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Pneumonia, Viral; Pneumonia; Lung Diseases; COVID-19
• Other Study ID Numbers: GOKAE-2020-190

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: Yes
• IPD Plan Description: All data other than the personal information of the participants can be shared.
• IPD Sharing Time Frame: Starting in January 2023
• IPD Sharing Access Criteria: IPDs will be shared openly to all scientists without patient names and personal information, and the corrospendence author will be responsible for sharing. All requests by researchers and scientists will be met.
• IPD Sharing Supporting Information Type: Study Protocol; Statistical Analysis Plan (SAP)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No