Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

Adegboye, Oyelola A.; Adekunle, Adeshina I.; Gayawan, Ezra

doi:10.3390/ijerph17093054

Cited by 107 publications

(101 citation statements)

References 17 publications

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“…For example, Ayinde et al [5] compared different statistical models and estimators in forecasting COVID-19 cases without accounting for the uncertainties associated with it. Adegboye et al [6] investigated the early COVID-19 transmissibility using the Bayesian method while incorporating serial distribution uncertainties, however without projections.…”

Section: Introductionmentioning

confidence: 99%

Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

Adeyeri

K.S.A.

Ige

et al. 2020

Preprint

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The World Health Organization (WHO) declared COVID-19 a global pandemic on 11 March 2020 due to its global spread. In Nigeria, the first case was documented on 27 February 2020. Since then, it has spread to most parts of the country. This study models, forecasts and projects COVID-19 incidence, cumulative incidence and death cases in Nigeria using six estimation methods i.e. the attack rate, maximum likelihood, exponential growth, Markov chain monte Carlo (MCMC), time-dependent and the sequential Bayesian approaches. A sensitivity analysis with respect to the mean generation time is used to quantify the associated reproduction number uncertainties. The relationship between the COVID-19 incidence and five meteorological variables are further assessed. The result shows that the highest incidences are recorded in days with either religious activities or market days while the weekday trend decreases towards the weekend. It is also established that COVID-19 incidence significantly increases with increasing sea level pressure (0.7 correlation coefficient) and significantly decreases with increasing maximum temperature (-0.3 correlation coefficient). Also, selecting an optimal period for reproduction number estimates reduces the variability between estimates. As an example, in the EG approach, the epidemic curve that optimally fits the exponential growth is between 1- and 53-time units with reproduction number estimate of 1.60 [1.58; 1.62] at 95% confidence interval. However, this optimal reproduction number estimate is different from the default reproduction number estimate. Using the MCMC approach, the correlation coefficients between the observed and forecasted incidence, cumulative death and cumulative confirmed cases are 0.66, 0.92 and 0.90 respectively. The projections till December shows values approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country.

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Section: Introductionmentioning

confidence: 99%

Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

Adeyeri

K.S.A.

Ige

et al. 2020

Preprint

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“…Although the exponential growth model also well t the early epidemic patterns of COVID-19 in some other regions like Europe and Africa, the estimates of the relevant parameters in the model varied from each other. This phenomenon could be related to different climatic conditions, genetic background and su ciency of health resources (especially detection capabilities) [7,8]. Our model predicted that the cumulative COVID-19 cases in Southeast Asia would exceed 10,000 by early April 2020.…”

Section: Discussionmentioning

confidence: 89%

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] March 2020 (second phase). Cases of COVID-19 during the rst phase of infections were relatively mild, with only a few con rmed cases reported daily, and most were from Singapore and Thailand.…”

mentioning

confidence: 99%

Early Epidemiological Features and Trends of the COVID-19 Outbreak in Southeast Asia: a Population-Level Observational Study

Zhu

Jirapat

Shen

et al. 2020

Preprint

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Background: The global outbreak of coronavirus disease 2019 (COVID-19) has been ongoing in Southeast Asia since 13 January 2020. We conducted an observational study to investigate underlying disease patterns of COVID-19 in Southeast Asia, and consequently to guide intervention strategies against the pandemic.Methods: In this population-level observational study set in Southeast Asia, we compiled a list of patients with COVID-19 (n = 925) and daily country-level case counts (n = 1346) from 13 January 2020 through 16 March 2020. All epidemiological data were extracted from official websites of the WHO and health authorities of each Southeast Asian country. Relevant spatiotemporal distributions, demographic characteristics, and short-term trends were assessed.Results: A total of 1,346 confirmed cases of COVID-19, with 217 (16.1%) recoveries and 18 (1.3%) deaths, were reported in Southeast Asia as of 16 March 2020. Early transmission dynamics were examined with an exponential regression model: y=0.30e0.13x (p<0·01, adjusted R2 = 0.96). Using this model, we predicted that the cumulative number of reported COVID-19 cases in Southeast Asia would exceed 10,000 by early April 2020. A total of 74 cities across eight countries in Southeast Asia were affected by COVID-19. Most of the confirmed cases were located in five international metropolitan areas. Demographic analyses of the 925 confirmed cases indicated a median age of 44 years and a sex ratio of 1.25. The median age of the local patient population was significantly higher than that of the corresponding country’s general population (p<0·01), whereas the sex ratio did not significantly differ.Conclusions: The COVID-19 situation in Southeast Asia is unevenly geographically distributed and pessimistic in the short term. Age may play a significant role in both the susceptibility to and outcome of infection. Real-time active surveillance and targeted intervention strategies are urgently needed to contain the pandemic.

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“…This knowledge had been successfully converted into healthy behavioral practices, such as using soap and immunizing children. Given the recent global outbreak of the novel coronavirus disease, the importance of enforcing these practices has taken on heightened importance for vulnerable populations such as the ones covered in our survey [22]. The high reported prevalence of diarrheal illness and measles highlights the continued need to leverage these areas of strength to further improve child health.…”

Section: Strengthsmentioning

confidence: 99%

Enhancing Knowledge in Informal Settlements: Assessing Health Beliefs and Behaviors in Nigeria

Liu¹,

Maki

Maitland

et al. 2020

Annals of Global Health

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Background: Community Health Education (CHE) programs have been shown to be effective in relieving the burden on healthcare systems in Sub-Saharan Africa. Objective: This project aimed to determine the baseline level of health literacy, behavioral practices, and accessibility to resources in a set of 16 informal settlements located around Lagos, Nigeria in order to identify topics that should be emphasized in a new teaching curriculum directed at local Community Health Educators. Methods: In June of 2017, a unique cross-sectional survey composed of 37 questions was conducted in informal settlements around Lagos. Sites selected were areas in which future CHE trainings were planned to take place and survey participants were chosen by trained community health educators based on convenience sampling with snowball effect. Survey questions included both multiple-choice and open-ended questions and were asked in the local language. We collected demographic information and assessed health literacy, health behaviors, and community practices. Results were analyzed using descriptive statistics to assess for differences between demographic groups. Findings: Our survey collected 348 total responses. Respondents displayed a high level of knowledge regarding the benefits of hand washing (97.1%) and childhood immunizations (81.0%). Knowledge around infectious diseases and reproductive health was lower, including a large proportion of people (50%) incorrectly indicating that HIV could be spread through a mosquito bite. Malaria was reported to be the most prevalent disease affecting both adults (40.0%) and children (58.3%). Health access was limited, with most people not reporting access to a nearby health center (55.8%). Conclusions: Areas of knowledge that should be emphasized in future versions of CHE training curricula include infectious diseases, reproductive health, and reinforcement of the importance of sanitation and clean water. The curriculum should address the reality of limited health access and develop strategies to improve this.

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Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

Cited by 107 publications

References 17 publications

Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

Early Epidemiological Features and Trends of the COVID-19 Outbreak in Southeast Asia: a Population-Level Observational Study

Enhancing Knowledge in Informal Settlements: Assessing Health Beliefs and Behaviors in Nigeria

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