An Efficient COVID-19 Prediction Model Validated with the Cases of China, Italy and Spain: Total or Partial Lockdowns?

Abstract

The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2): What Can Africa Learn from Ongoing Research in Epidemiology, Clinical Manifestations, Chemotherapy and Prevention?

“… For health facilities in affected countries, increasing bed capacity to isolate COVID-19 cases is important as this helped to contain the widespread of the disease in Wuhan in China [12].  Data driven models indicates that the lock down policy successfully helped to curb the spread of COVID-19 in China, Italy, and Spain [12,59,60]. Based on the consequences of lockdown in Africa such as economic hardship, most countries have not been able to include this approach in their list of measures to stamp out the disease from the continent.…”

Exploring the Impact of Restricted Partners’ Visiting Policies on Non-Infected Mothers’ Mental Health and Breastfeeding Rates during the COVID-19 Pandemic

“…As general lockdown and social distancing were proclaimed [2], studies conducted worldwide began to show the impact of these practices on mental health, reporting an increased rate of anxiety symptoms and high-stress levels [3]. Pregnant women and newly mothers' well-known increased risk for anxiety and depression exposes them to an even higher risk of psychological distress during a pandemic [4,5].…”

Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China

“…While no therapeutics have yet been proven effective and the true pathogenetic mechanisms of COVID-19 are not fully understood, the identification of risk factors in predicting the occurrence of critical illness in patients with COVID-19 is crucial to developing prevention strategies. Currently, several prediction models for COVID-19 diagnosis and prognosis have been established [ 7 – 10 ], with no consensus on their implications. Hence, the identification and characterization of risk profiling for the onset and progression of COVID-19 symptoms is still subject to exploration, improvement, and renewal.…”