Estimation of the case fatality rate of COVID-19 epidemiological data in Nigeria using statistical regression analysis

As the COVID-19 pandemic marches exponentially, epidemiological data is of high importance to analyse the current situation and guide intervention strategies. This study analyses the epidemiological data of COVID-19 from 17 countries, representing 85 per cent of the total cases within first 90 days of lockdown in Wuhan, China. It follows a population-level observational study design and includes countries with 20,000 cases (or higher) as of 21 April 2020. We sourced the data for these 17 countries from worldometers. info, a digital platform being used by several media and reputed academic institutions worldwide. We calculated the prevalence, incidence, case fatality rate and trends in the epidemiology of COVID-19, and its correlation with population density, urbanisation and elderly population. The analysis represents 85 per cent ( N = 2,183,661) of all cases within the first 90 days of the pandemic. Across the analysed period, the burden of the pandemic primarily focused on high- and middle-income countries of Asia, Europe and North America. While the total number of cases and deaths are highest in USA, the prevalence, incidence and case fatality rates are higher in the European countries. The prevalence and incidence vary widely among countries included in the analysis, and the number of cases per million and the case fatality rate are correlated with the proportion of the elderly population and to a lesser extent with the proportion of the urban population.

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Infection pattern, case fatality rate and spread of Lassa virus in Nigeria

BMC Infectious Diseases ◽

10.1186/s12879-021-05837-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Clement Ameh Yaro ◽

Ezekiel Kogi ◽

Kenneth Nnamdi Opara ◽

Gaber El-Saber Batiha ◽

Roua S. Baty ◽

...

Keyword(s):

Case Fatality Rate ◽

Control Strategies ◽

Case Fatality ◽

Epidemiological Data ◽

Integrated Approach ◽

Lassa Fever ◽

Lassa Virus ◽

Fatality Rate ◽

Wet Season ◽

Infection Pattern

Abstract Background Lassa fever (LF) is a zoonotic infectious disease of public concern in Nigeria. The infection dynamics of the disease is not well elucidated in Nigeria. This study was carried out to describe the pattern of infection, case fatality rate and spread of lassa virus (LASV) from 2017 to 2020. Methods Weekly epidemiological data on LF from December, 2016 to September, 2020 were obtained from Nigeria Centre for Disease Control. The number of confirmed cases and deaths were computed according to months and states. Descriptive statistics was performed and case fatality rate was calculated. Distribution and spread maps of LF over the four years period was performed on ArcMap 10.7. Results A total of 2787 confirmed cases and 516 deaths were reported in Nigeria from December, 2016 to September, 2020. Increase in number of cases and deaths were observed with 298, 528, 796 and 1165 confirmed cases and 79, 125, 158 and 158 deaths in 2017, 2018, 2019 and 2020 respectively. Over 60% of the cases were reported in two states, Edo and Ondo states. The LF cases spread from 19 states in 2017 to 32 states and Federal Capital Territory (FCT) in 2020. Ondo state (25.39%) had the highest of deaths rate from LF over the four years. Case fatality rate (CFR) of LF was highest in 2017 (26.5%) with CFR of 23.7, 19.6 and 13.4% in 2018, 2019 and 2020 respectively. The peak of infection was in the month of February for the four years. Infections increases at the onset of dry season in November and decline till April when the wet season sets-in. Conclusion There is an annual increase in the number of LASV infection across the states in Nigeria. There is need to heighten control strategies through the use of integrated approach, ranging from vector control, health education and early diagnosis.

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COVID19 - A Correlation Study of Infection Fatality Rate vs Age

10.21203/rs.3.rs-85482/v3 ◽

2021 ◽

Author(s):

JAYDIP DATTA

Keyword(s):

Critical Care ◽

Regression Analysis ◽

Logistic Model ◽

Case Fatality Rate ◽

Linear Regression Analysis ◽

Case Fatality ◽

Correlation Study ◽

Asymptomatic Infection ◽

Fatality Rate ◽

Special Case

Abstract In this article one of the most important epidemiological parameter ie Infection fatality rate [ 1 ] is correlated with age of the population through a sigmoid statistics of Logistic model. The IFR is a special case of case fatality rate ( CFR ) . The CFR ( 1 ) is termed as the number of deaths due to symptomatic Covid infection within entire population per unit time . The IFR is a special case of CFR where number of deaths to be considered as total number of deaths due to symptomatic as well as asymptomatic infection within the same population per unit time .The sigmoid fit can also be approximated to modified quadratic fit [ 4-5 ]. CFR can be more specifically correlated to comorbidities [8 ]through linear regression analysis. co morbidities due to SARS-COV-2 infection for different chronic diseases like heart , Lung , Kidney , related chronic failure are analysed by a significant Pearson statistics ( 10 ) are discussed here . The IFR can be realised from mild to hospitalisation under ICU , critical care and finally severity to death( 9,12).

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Burden and Trend of Measles in Nigeria: Five-year Review Case-base Surveillance Data

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v10i1.8756 ◽

2018 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Ibrahim S. Baffa ◽

Yahaya Mohammed ◽

Rabi Usman ◽

Aisha Abubakar ◽

Patrick Nguku

Keyword(s):

Disease Surveillance ◽

Case Fatality Rate ◽

Case Fatality ◽

Secondary Data ◽

Epidemiological Data ◽

Laboratory Diagnosis ◽

Northern Region ◽

World Health ◽

Fatality Rate ◽

Health Organization

ObjectiveWe reviewed measles specific Integretaged Disease Surveillance and Response (IDSR) data from Nigeria over a five-year period to highlights its burden and trends, and make recommendations for improvements.IntroductionMeasles is a vaccine preventable, highly transmissible viral infection that affects mostly under-five year children. The disease is caused by a Morbillivirus; member of the Paramyxovirus family.MethodsWe conducted a secondary data analysis of measles specific IDSR records of all States in Nigeria from January 2012 to September 2016. The record had reported measles cases with laboratory outcomes from all the States. IDSR weekly epidemiological data were obtained from Surveillance Unit, Nigerian Centre for Disease Control (NCDC).ResultsA total of 131,732 cases were recorded within the period. Highest number of cases 57,892(43.95%) were recorded in 2013 while the least number of cases 11,061(8.4%) were recorded in 2012. A total of 817 deaths were recorded, given a case fatality rate (CFR) of 0.62%. The CFR showed a decreasing trend over the years with the highest CFR (1.43%) recorded in 2012 and the least CFR (0.44%) recorded in 2016. Only 8,916 (6.7%) cases were confirmed by laboratory investigation. The Northwest region recorded the highest attack rate (AR) of 149.7 cases per 100,000 population, followed by the Northeast region with 140.2 cases per 100,000 population, while the South-south region recorded the least AR of 15.8 cases per 100,000 population. Case Fatality Rate per region followed similar pattern, with the Northcentral region having the highest CFR of 4.38%. The trend of measles cases followed the same pattern. Cases peaked at March, then gradually reduced to lowest level at June.ConclusionsMeasles infection remains a burden especially in the northern region of Nigeria. Though measles fatalities were on decline over the years, laboratory diagnosis of cases has been suboptimal. We recommended improvement on routine immunization and measles case management, and strengthening of regional laboratories capacity for measles diagnosis.References1. WHO | Measles. WHO [Internet]. World Health Organization; 2017 [cited 2017 Apr 10]; Available from: http://www.who.int/mediacentre/factsheets/fs286/en/2. Akande TM. A review of measles vaccine failure in developing countries. Niger. Med. Pract. SAME Ventures; 2007;52:112–6.3. Ibrahim BS, Gana GJ, Mohammed Y, Bajoga UA, Olufemi AA, Umar AS, et al. Outbreak of measles in Sokoto State North-Western Nigeria, three months after a supplementary immunization campaign: An investigation report 2016. Australas. Med. J. AUSTRALASIAN MEDICAL JOURNAL PTY LTD HILLARYS, GPO BOX 367, PERTH, WA 6923, AUSTRALIA; 2016;9:324–35.

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Identification of spatial variations in COVID-19 epidemiological data using K-Means clustering algorithm: a global perspective

10.1101/2020.06.03.20121194 ◽

2020 ◽

Author(s):

Viswa Chandu

Keyword(s):

Public Health ◽

Health Expenditure ◽

Case Fatality Rate ◽

Case Fatality ◽

Epidemiological Data ◽

Fatality Rate ◽

Background Characteristics ◽

Public Health Expenditure ◽

Cluster 2 ◽

Country Specific

Background: Discerning spatial variations of COVID-19 through quantitative analysis operating on the geographically designated datasets relating to socio-demographics and epidemiological data facilitate strategy planning in curtailing the transmission of the disease and focus on articulation of necessary interventions in an informed manner. Methods: K-means clustering was employed on the available country-specific COVID-19 epidemiological data and the influential background characteristics. Country-specific case fatality rates and the average number of people tested positive for COVID-19 per every 10,000 population in each country were derived from the WHO COVID-19 situation report 107, and were used for clustering along with the background characteristics of proportion of countrys population aged >65 years and percentage GDP spent as public health expenditure. Results: The algorithm grouped the 89 countries into cluster 1 and Cluster 2 of sizes 54 and 35, respectively. It is apparent that Americas, European countries, and Australia formed a major part of cluster 2 with high COVID-19 case fatality rate, higher proportion of countrys population tested COVID-19 positive, higher percentage of GDP spent as public health expenditure, and greater percentage of population being more than 65 years of age. Conclusion: In spite of the positive correlation between high public health expenditure (%GDP) and COVID-19 incidence, case fatality rate, the immediate task ahead of most of the low and middle income countries is to strengthen their public health systems realizing that the correlation found in this study could be spurious in light of the underreported number of cases and poor death registration.

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Regression Analysis of COVID-19 Spread in India and its Different States

10.1101/2020.05.29.20117069 ◽

2020 ◽

Cited By ~ 3

Author(s):

Poonam Chauhan ◽

Ashok Kumar ◽

Pooja Jamdagni

Keyword(s):

Regression Analysis ◽

Regression Model ◽

Tamil Nadu ◽

Polynomial Regression ◽

Case Fatality ◽

Epidemiological Data ◽

Fatality Rate ◽

Indian States ◽

Number Of Patients ◽

Polynomial Regression Model

AbstractLinear and polynomial regression model has been used to investigate the COVID-19 outbreak in India and its different states using time series epidemiological data up to 26th May 2020. The data driven analysis shows that the case fatality rate (CFR) for India (3.14% with 95% confidence interval of 3.12% to 3.16%) is half of the global fatality rate, while higher than the CFR of the immediate neighbors i.e. Bangladesh, Pakistan and Sri Lanka. Among Indian states, CFR of West Bengal (8.70%, CI: 8.21–9.18%) and Gujrat (6.05%, CI: 4.90–7.19%) is estimated to be higher than national rate, whereas CFR of Bihar, Odisha and Tamil Nadu is less than 1%. The polynomial regression model for India and its different states is trained with data from 21st March 2020 to 19th May 2020 (60 days). The performance of the model is estimated using test data of 7 days from 20th May 2020 to 26th May 2020 by calculating RMSE and % error. The model is then used to predict number of patients in India and its different states up to 16th June 2020 (21 days). Based on the polynomial regression analysis, Maharashtra, Gujrat, Delhi and Tamil Nadu are continue to remain most affected states in India.

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Assessment of Epidemiological Determinants of COVID-19 Pandemic Related to Social and Economic Factors Globally

Journal of Risk and Financial Management ◽

10.3390/jrfm13090194 ◽

2020 ◽

Vol 13 (9) ◽

pp. 194

Author(s):

Mohammad Mahmudul Hassan ◽

Md. Abul Kalam ◽

Shahanaj Shano ◽

Md. Raihan Khan Nayem ◽

Md. Kaisar Rahman ◽

...

Keyword(s):

Regression Analysis ◽

Population Density ◽

Recovery Rate ◽

Attack Rate ◽

Case Fatality Rate ◽

Case Fatality ◽

Fatality Rate ◽

Middle Income ◽

Middle Income Countries ◽

The Impact

The COVID-19 pandemic has manifested more than a health crisis and has severely impacted on social, economic, and development crises in the world. The relationship of COVID-19 with countries’ economic and other demographic statuses is an important criterion with which to assess the impact of this current outbreak. Based on available data from the online platform, we tested the hypotheses of a country’s economic status, population density, the median age of the population, and urbanization pattern influence on the test, attack, case fatality, and recovery rates of COVID-19. We performed correlation and multivariate multinomial regression analysis with relative risk ratio (RRR) to test the hypotheses. The correlation analysis showed that population density and test rate had a significantly negative association (r = −0.2384, p = 0.00). In contrast, the median age had a significant positive correlation with recovery rate (r = 0.4654, p = 0.00) and case fatality rate (r = 0.2847, p = 0.00). The urban population rate had a positive significant correlation with recovery rate (r = 0.1610, p = 0.04). Lower-middle-income countries had a negative significant correlation with case fatality rate (r= −0.3310, p = 0.04). The multivariate multinomial logistic regression analysis revealed that low-income countries are more likely to have an increased risk of case fatality rate (RRR = 0.986, 95% Confidence Interval; CI = 0.97−1.00, p < 0.05) and recovery rate (RRR = 0.967, 95% CI = 0.95–0.98, p = 0.00). The lower-income countries are more likely to have a higher risk in case of attack rate (RRR = 0.981, 95% CI = 0.97–0.99, p = 0.00) and recovery rate (RRR = 0.971, 95% CI = 0.96–0.98, p = 0.00). Similarly, upper middle-income countries are more likely to have higher risk in case of attack rate (RRR = 0.988, 95% CI = 0.98–1.0, p = 0.01) and recovery rate (RRR = 0.978, 95% CI = 0.97–0.99, p = 0.00). The low- and lower-middle-income countries should invest more in health care services and implement adequate COVID-19 preventive measures to reduce the risk burden. We recommend a participatory, whole-of-government and whole-of-society approach for responding to the socio-economic challenges of COVID-19 and ensuring more resilient and robust health systems to safeguard against preventable deaths and poverty by improving public health outcomes.

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Impact of COVID-19 Pandemic on Refugees, Migrants and Asylum Seekers Living in Camps and Reception and Identification Centers in Greece in the Pre-Vaccination Period, February 2020 to May 2021. Summary of Epidemiological Findings

10.21203/rs.3.rs-911182/v1 ◽

2021 ◽

Author(s):

Spiros Sapounas ◽

Konstantinos Mitrou ◽

Alexandros Georgios Asimakopoulos ◽

Garyfallia Antoniou ◽

Ioanna Papari ◽

...

Keyword(s):

General Population ◽

Asylum Seekers ◽

Case Fatality Rate ◽

Clinical Manifestations ◽

Case Fatality ◽

Population Data ◽

Epidemiological Data ◽

P Value ◽

Fatality Rate ◽

Notification Rate

Abstract Background: Protection of refugees, migrants, and asylum seekers living in open hosting camps (HCs) and reception and identification centers (RICs) has been a priority since the beginning of the COVID-19 pandemic. We present the epidemiological data of COVID-19 infection in HCs/RICs in Greece from February 2020 to May 2021, before the initiation of the onsite vaccinations.Methods: Case confirmation was performed by rapid antigenic test and/or RT-PCR. Data were retrieved from the National COVID-19 registry. The notification rate by type of accommodation facility, by sex and ethnicity and the mean age of cases, were calculated for HCs, RICs and general population. Data on clinical manifestations, and disease severity (admissions to intensive care unit (ICU) / case fatality rate) were analysed.Results: Of the 397,497 recorded domestic COVID-19 infection cases, 2,609 (0.7%) regarded HCs/RICs; of them 1,566 (60%) were identified in 27 HCs and 1,043 (40%) in six RICs. The notification rate was 542 and 380 cases per 10,000 population in HCs/RICs and the general population, respectively (p-value<0.001).Up to February 2021 the occurrence of cases in HCs/RICs did not follow the occurrence of cases in the general population. After March 2021 the course of the outbreak in HCs/RICs and the general population was similar.The median age of cases in HCs/RICs and the general population was 27 (range:0-81) and 44 (range:0-106), respectively (p<0.001). Twenty-four different ethnicities were reported among migrant cases; 51% were from Afghanistan, 13% from Syria, 6% from Kongo and 5% from Somalia.Overall, 48% and 80% of cases, respectively in HCs/RICs and the general population were symptomatic (p<0.001). Five (0.2%) cases in HCs/RICs were admitted to the ICU compared to 10,426 cases (3.0%) in the general population (p-value <0.001). Case fatality rate was 3% in the general population and 0.08% in HCs/RICs (p-value <0.001).Conclusion: Recorded COVID-19 infections were less severe in migrants living at HCs/RICs than the general population, however, the number of identified cases was high and measures for the prevention of transmission should be strengthened.

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Clinical and Epidemiological Data of COVID-19 from Regensburg, Germany – A Retrospective analysis of 1084 consecutive cases

10.21203/rs.3.rs-103516/v1 ◽

2020 ◽

Author(s):

Benedikt M J Lampl ◽

Matthias Buczovsky ◽

Gabriele Martin ◽

Helen Schmied ◽

Michael Leitzmann ◽

...

Keyword(s):

Risk Factors ◽

Case Fatality Rate ◽

Case Fatality ◽

Epidemiological Data ◽

Clinical Severity ◽

Symptom Duration ◽

Fatality Rate ◽

Duration Of Symptoms ◽

Chronic Heart Disease

Abstract Background: COVID-19 is a new syndrome caused by the recently emerged SARS-CoV-2. We collected clinical and epidemiologic data in an almost complete cohort of SARS-CoV-2 positive individuals from Regensburg, Germany, from March 2020 to May 2020.Methods: Retrospective cohort of consecutive COVID-19 cases recorded between March 7, 2020 and May 24, 2020 as part of an infection control investigation program, with prospective follow-up interviews gathering information on type and duration of symptoms and COVID-19 risk factors until June 26, 2020.Results: Of 1,089 total cases, 1,084 (99.6%) cases were included. The incidence during the time period was 315.4/100,000, lower than in the superordinate government district Oberpfalz (468,5/100,000) and the overall state of Bavaria (359.7/100,000). The case fatality rate was 2.1%. Among fatal cases, the mean age was 74.4 years and 87% presented with known risk factors, most commonly chronic heart disease, chronic lung disease, kidney disease, and diabetes mellitus. 897 cases (82,7%) showed at least one symptom, most frequently cough (45%) and fever (41%). Further, 18% of cases suffered from odour/taste disorder. 17% of total cases reported no symptoms. The median duration of general illness was 10 days. During follow-up, 8,9% of 419 interviewed cases reported at least one symptom lasting at least 6 weeks, and fatigue was the most frequent persistent symptom. Discussion: We report data on type and duration of symptoms, and clinical severity of nearly all (99,5%) patients with SARS-CoV-2 recorded from March 2020 to May 2020 in Regensburg. A broad range of symptoms and symptom duration was seen, some of them lasting several weeks. The case fatality rate was 2.1%. Asymptomatic cases may be underrepresented due to the nature of the study.

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