AIPMED

ANNALS OF IBADAN POSTGRADUATE MEDICINE

PREDICTING COVID-19 SECOND WAVE SIGNAL IN SELECTED STATES OF SOUTHWEST NIGERIA: A COMPARISON OF CUMULATIVE SUM C2 AND CUMULATIVE SUM C1 EPIDEMIC THRESHOLDS

S. Bello and M.M. Salawu

Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Nigeria


ABSTRACT

Introduction: Epidemic thresholds generated using the conventional historical data is not optimal for COVID-19 because of its short historical trajectory. This study therefore, aimed to develop and compare Cumulative sum C2 and C1 epidemic thresholds for COVID-19 in selected states in southwestern Nigeria.

Methods: This was a retrospective longitudinal analysis of the COVID-19 surveillance data (week 10 – 48) retrieved from the Nigerian Centre for Disease Control (NCDC) database of situation reports as at the 6th of December, 2020. Data was managed with Microsoft excel. The weekly time scale was adopted for developing the CUSUM C2 and C1 epidemic thresholds for three selected southwest states and Nigeria.

Results: A total of 236 situation reports were reviewed for each state. For Lagos state, the maximum C2 and C1 estimated was 2326 which was during the peak of the epidemic. From the four most recent surveillance points, the thresholds and the observed confirmed cases appeared to diverge from each other. For Ogun state, the maximum C2 and C1 estimated was 318 during the peak of the epidemic. From the four most recent surveillance points, the thresholds and the observed confirmed cases appeared to converge. For Oyo state, the maximum C2 and C1 estimated was 708 during the peak of the epidemic. From the four most recent surveillance points, the thresholds and the observed confirmed cases appeared to converge and then diverge.

Conclusion: A closer monitor of the surveillance data for the states is recommended for a possible public health intervention.

Correspondence

Dr. S. Bello

Department of Epidemiology and

Medical Statistics,

Faculty of Public Health,

College of Medicine,

University of Ibadan,

Nigeria

Email: drsegunbello@yahoo.com


BACKGROUND

Coronavirus disease (COVID-19) is a pandemic that spreads through close contact and via respiratory droplets produced when people cough or sneeze.1 This novel strain of coronavirus, severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), has not been identified in humans until January, 2020 when it was isolated, confirming the circulation of a new respiratory illness, and named coronavirus disease 2019.2 COVID-19 has since been spreading rapidly to involve most nations of the world and the World Health Organization (WHO) declared COVID-19 a public health emergency of international concern on the 30th January, 2020.3 Globally, over 63 million (63,691,642) people have been infected with over one million (1,476,277) death.4 Currently, Nigeria has reported over 67, 000 cases of COVID-19 with over 1,000 deaths.

The World Health Organization (WHO) has instituted public health and social measures to slow down the spread or completely stop the chains of transmission of COVID-19 outbreak at international, national and community levels.6, 7 These are individual measures such as social and physical distancing measures between people, use of facemask and reduce contact with contaminated surfaces, frequent hand washing and cough etiquette. Environmental measures to curtail this outbreak include detecting and isolating cases, contacttracing and quarantine.5, 7

COVID-19 is a new infectious disease with a new virus strain which requires adequate surveillance for monitoring and early detection of spikes or increase in cases. The conventional disease surveillance system involves continuous collection, analysis and interpretation of large volumes of data of diseases and health related events to enable prompt intervention for disease control.8, 9 This system is inadequate for a public health emergency like COVID–19 which requires an early warning system for immediate identification of cases meant for prompt intervention.10

Epidemiological data on COVID-19 globally has been collected for less than a full year. Thus, methods to develop epidemic thresholds that require considerable historical data would not perform optimally for the development of COVID-19 epidemic thresholds for the infection. A more robust, quick, timely, efficient, sensitive and specific method of developing epidemic thresholds appears more appropriate at this time.

Variants of the cumulative sum (CUSUM) method for developing epidemic thresholds appear to be best suited for COVID-19 data because they are more sensitive and specific and best suited for a short baseline historical data compared to the historical limit’s method. The cumulative sum (C-SUM) method for epidemic detection is based on computing moving averages for specified surveillance data points.11 The objective of this study was therefore, to develop and compare epidemic thresholds for COVID-19 in selected states in southwestern Nigeria using the cumulative sum C2 and C1 methods.