Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, A Modelling Study

Abstract

Background: A new type of coronavirus (later named Sars-Cov-2) drew attention in 31 December 2019 after the reporting of 27 unidentified pneumonia cases detected in Wuhan, China to the World Health Organization (WHO). Rapid progression of the COVID-19 pandemic has revealed the necessity of epidemic modeling studies to evaluate the course of the epidemic and its burden on the health system. This study aims to estimate the total number of infected people, evaluate the consequences of social interventions on the healthcare system and predict the expected number of cases, intensive care needs, hospitalizations and mortality rates in Turkey according to possible scenarios via the SEIR-based epidemic modeling method. Methods:This study was carried out in three dimensions. In the first, the actual number of people infected in the community has been estimated using the number of deaths in Turkey. In the second, the expected total numbers of infected people, total deaths, total hospitalizations, and intensive care unit (ICU) bed needs have been predicted in case of no intervention. In third, distribution of the expected number of infected people and deaths, ICU and non-ICU bed needs over time has predicted based on SEIR modelling. A simulator (TURKSAS) has been developed and predictions made in 4 scenarios for Turkey.Results: According to deaths, estimated number of infected people in Turkey on March 21 was 123,030.In the case of no intervention (1 st scenario) the expected total number of infected people is 72,091,595, the total number of deaths is 445,956, the attack rate is 88.1%, the mortality ratio is 0.54%. The ICU bed capacity in Turkey is expected to exceed 4.4-fold and non-ICU bed capacity exceed 3.21fold. In 2 nd and 3 rd scenario according to the calculations made by considering the social compliance rates of the NPIs, the value of R0 is estimated to decrease from 3 to 1.38 level. Compliance with NPIs makes a 94,303 difference in the expected number of deaths. In both scenarios, the predicted peak value of occupied ICU and non-ICU beds remains below the Turkey's capacity. While this study conducted, curfew for >65 and <20 age groups was in force in Turkey. If the curfew is declared for the 21-64 age population (4 th scenario), the R0 value drops below 1 (0.98), the expected deaths are 14,230 and the peak values of daily ICU and non-ICU bed demand are below the country's capacity. Discussion:Modeling epidemics with assumptions supported by scientific literature and establishing decision support systems based on objective criteria is an important requirement. According to scientific data for the population of Turkey, the situation is not expected to be of worse than predictions presented in the second dimension. Predictions show that 16 million people can be prevented from being infected and 100,000 deaths can be prevented by full compliance with the measures taken. Complete control of the pandemic is possible by keeping R0 below 1. For this, additional evidence-based measures are needed.

O potencial de propagação da COVID-19 e a tomada de decisão governamental: uma análise retrospectiva em Florianópolis, Brasil

“…There is robust evidence regarding the effectiveness of non-pharmacological measures, especially social distancing, in controlling the spread of SARS-CoV-2. [3][4][5][6][7] Suppression strategies have achieved good results, as seen in New Zealand 30,31 . This evidence, however, indicates the maximum potential impact of the actions if implemented in due time.…”

“…Additionally, the use of face masks and good hand hygiene are recommended 1 . There is evidence that non-pharmacological measures are effective in controlling the transmission of COVID-19 [3][4][5][6][7] . They decrease the transmission of the disease and the number of serious cases, thereby reducing the impact of the disease on the health system and decreasing the number of people who die and the number of survivors with side effects.…”

Estimating Underdiagnosis of Covid-19 With Nowcasting and Machine Learning – Experience From Brazil

“…9 Nonpharmacological measures have been shown to be effective in controlling the transmission of COVID-19. [10][11][12][13][14] They can reduce the impact in the health system, given managers time to organize properly the system. These measures also reduce the need for hospitalization by other conditions that could compete for beds with SARS-CoV-2 patients.…”

“…16 Nowcasting approaches try to estimate the number of a given event in the present. 8,10,17 This strategy has been used to improve surveillance of infectious diseases like AIDS 18,19 cholera, 20 influenza infections 8,21 and, recently, COVID-19. 3,8,17,20,22 Nowcasting techniques, in general, uses time-series predictions.…”

“…No single set of interventions is appropriate to all contexts owing to the combination of these factors with climatic, demographic and organization issues of each country. 10 Thus, monitoring on near real time should be a key part of the strategy to couple with SARS-CoV2. Among the challenges for timely monitoring are delays in providing medical care after onset of the symptoms and delays in diagnosis.…”