Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries

Stübinger, Johannes; Schneider, Lucas

doi:10.3390/healthcare8020099

Cited by 70 publications

(60 citation statements)

References 51 publications

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“…The circumstantial evidence that links the first case of COVID-19 to the Huanan South Seafood Market that sells various exotic live animals suggests that the zoonotic Coronavirus crossed the barrier from animal to human at this wet market. 2 It has since become a global public health emergency. 3 The World Health Organization (WHO) designated COVID-19 a pandemic on March 11, 2020.…”

Section: Introductionmentioning

confidence: 99%

<p>Predictors of Non-Adherence to Public Health Instructions During the COVID-19 Pandemic in the Democratic Republic of the Congo</p>

Nzaji¹,

Mwamba²,

Miema³

et al. 2020

JMDH

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Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: https://youtu.be/RBTcuSkgNco Background: Adherence to public health instructions for the COVID-19 is important for controlling the transmission and the pandemic's health and economic impacts. The aim of this study was to determine the associated factors of non-adherence to public health and social measures instructions. Methods: This was a cross-sectional study conducted with 1913 participants in two provinces of DRC, Mbuji-Mayi, and Kamina. Predictors of non-adherence to COVID-19 preventive measures were identified using binary logistic regression analysis. P-value<0.05 was considered as a significant predictor. Results: Among 1913 participants (1057 [55.3%] male, age 34.1 [14.9] years), 36.6% were defined as non-adherents. Non-adherence was associated with never studied and primary education level [adjusted odds ratio (aOR)=1.63, CI=1.31-2.03], unemployed status [aOR=1.29, CI=1.01-1.67], living in Kamina (Haut-Lomami province) [aOR=1.63, CI=1.31-2.03], female gender of head of household [aOR=1.53, CI=1.16-2.03], no attending lectures/discussions about COVID-19 [aOR=1.61, CI=1.08-2.40], not being satisfied with the measures taken by the Ministry of Health [aOR=2.26, CI=1.78-2.81], not been regularly informed about the pandemic [aOR=2.25, CI=1.80-2.03], and bad knowledge about COVID-19 [aOR=2.36, CI=1.90-2.93]. Conclusion: The rate of non-observance of preventive measures for the COVID-19 pandemic is high, and different factors contributed. The government has to counsel the permanent updating of messages taking into account the context and the progress of the pandemic by using several communication channels.

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Section: Introductionmentioning

confidence: 99%

<p>Predictors of Non-Adherence to Public Health Instructions During the COVID-19 Pandemic in the Democratic Republic of the Congo</p>

Nzaji¹,

Mwamba²,

Miema³

et al. 2020

JMDH

View full text Add to dashboard Cite

show abstract

“…The occurrence of COVID-19 and past deadly coronaviruses, together with the mode of spread of these viruses as outlined below, are not contested. The focus has been on the epidemiology of COVID-19 [ 14 , 15 , 16 , 17 ] and weak health systems [ 6 ]. The mobilising narrative has been around health security [ 18 , 19 ], and thus emphasis has been put on public health measures to prevent, detect, verify, assess, and respond to the pandemic, as stipulated by international health regulations [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

The COVID-19 Pandemic and the Pathology of the Economic and Political Architecture in Cameroon

Ojöng

2020

Healthcare

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This article examines the factors restricting an effective response to the COVID-19 pandemic in Cameroon. It argues that structural adjustment policies in the 1980s and 1990s as well as corruption and limited investment in recent times have severely weakened the country’s health system. This article also emphasises the interconnection between poverty, slums, and COVID-19. This interconnection brings to the fore inequality in Cameroon. Arguably, this inequality could facilitate the spread of COVID-19 in the country. This article draws attention to the political forces shaping the response to the pandemic and contends that in some regions in the country, the lack of an effective response to the pandemic may not necessarily be due to a lack of resources. In so doing, it critiques the COVID-19 orthodoxy that focuses exclusively on the pathology of the disease and advocates “technical” solutions to the pandemic, while ignoring the political and socio-economic forces that shape the fight against the pandemic. At times, medical supplies and other forms of assistance may be available, but structural violence impairs access to these resources. Politics must be brought into the COVID-19 discourse, as it shapes the response to the pandemic.

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“…This approach is opposite to S-Curve (logistic curve) approach that assumes convergence. Stubinger and Schneider [ 26 ] argue that the Covid-19 spread in China first and then other countries got affected by it. Therefore, there are lead-lag effects between spreads of Covid-19 in different countries.…”

Section: Taxonomy Of Machine Learning Methodsologies For the Predictiomentioning

confidence: 99%

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

Ahmad

Garhwal

Ray

et al. 2020

Arch Computat Methods Eng

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Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

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Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries

Cited by 70 publications

References 51 publications

<p>Predictors of Non-Adherence to Public Health Instructions During the COVID-19 Pandemic in the Democratic Republic of the Congo</p>

<p>Predictors of Non-Adherence to Public Health Instructions During the COVID-19 Pandemic in the Democratic Republic of the Congo</p>

The COVID-19 Pandemic and the Pathology of the Economic and Political Architecture in Cameroon

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

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