Epidemic curves made easy using the R package incidence

Kamvar, Zhian N.; Cai, Jun; Pulliam, Juliet R. C.; Schumacher, Jakob; Jombart, Thibaut

doi:10.12688/f1000research.18002.1

Cited by 47 publications

(44 citation statements)

References 30 publications

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“…A sensitivity analysis was carried out to investigate the effect of changes in the sliding window on cases R e f f . All analyses were done in R software version 3.6.2 [12] using packages incidence [13] and EpiEstim [10].…”

Section: Methodsmentioning

confidence: 99%

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

Adegboye

Adekunle

Gayawan

2020

IJERPH

107

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On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05-0.10) with a doubling time of 9.84 days (95% CI: 7.28-15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65-8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83-7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26-1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

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Section: Methodsmentioning

confidence: 99%

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

Adegboye

Adekunle

Gayawan

2020

IJERPH

107

View full text Add to dashboard Cite

show abstract

“…Subsequently, based on the epidemic curve from the initial and time-dependent reproduction number estimations (as described below), the disease transmissibility and death cases are modelled and forecasted. For all time-dependent reproduction number estimations, a weekly sliding window is applied to the incidence series [8,9]. The model outputs are validated for one month.…”

Section: Methodsmentioning

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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“…Social distancing requires individuals to restrict group sizes to 10 or less and maintain a physical distance of 6 feet or greater. This widely adopted protocol was designed to achieve a manageable ratio of COVID‐19 cases to available healthcare resources (i.e., “flatten the curve”) 5‐7 . Several countries that have implemented social distancing demonstrate its potential for success.…”

Section: Introductionmentioning

confidence: 99%

Modifications of emergency dental clinic protocols to combat COVID‐19 transmission

Long

Ward

Pruett

et al. 2020

Special Care in Dentistry

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During the COVID-19 pandemic, incidence rates for dental diseases will continue unabated. However, the intent to prevent the spread of this lethal respiratory disease will likely lead to reduced treatment access due to restrictions on population movements. These changes have the potential to increase dental-related emergency department visits and subsequently contribute to greater viral transmission. Moreover, dentists experience unique challenges with preventing transmission due to frequent aerosol-producing procedures. This paper presents reviews and protocols implemented by directors and residents at the Dental College of Georgia to manage a dental emergency clinic during the COVID-19 pandemic. The methods presented include committee-based prioritization of dental patients, a multilayered screening process, team rotations with social and temporal spacing, and modified treatment room protocols. These efforts aid in the reduction of viral transmission, conservation of personal protective equipment, and expand provider availability. These protocols transcend a university and hospital-based models and are applicable to private and corporate models.

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Epidemic curves made easy using the R package incidence

Cited by 47 publications

References 30 publications

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

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

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

Modifications of emergency dental clinic protocols to combat COVID‐19 transmission

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