Keyword: SARS-CoV-2; COVID-19; Machine Learning; Pandemic
Introduction
Coronaviruses are the common pathogens responsible for infections like cough and cold next to rhinovirus. Recently the family added its seventh generation coronavirus SARS-CoV-2, framed to be pandemic by World Health Organization (WHO) having a close to 97 % similarity to SARS-CoV [1][2]. Unlike, Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) epidemic outbreak in 2003 and 2012, SARC-CoV-2 also known as COVID-19 mutated to transmit from animal to human. This virus is assumed to be transferred to humans from bats sold at a meat market in Wuhan, China. [3] The virus is coined deadly because of the ease of spreadability infecting around 552,598 people in 197 Countries with an exponential growth in two months leading to global shutdowns. The number of deaths marked 33,553 as on 29th February 2020 (WHO, 2020). The mortality rate is found to be very low, with 3% of the infected, when compared to earlier generation coronaviruses. MERS and SARS had a 30% and 10% death rate respectively. Howereve, the transmissibility of the COVID-19 is severe considering the way it easily spreads and people being passive carriers as well [5]. There are few medicines suggested to control the effects, but no vaccine has been found till date for coronavirus family, including COVID-19 [6] [7]. This is infact the largest single-strand RNA virus known to the humankind as where all the other viruses have a single protein spike that gets attached to the human cell, this coronavirus family hold 10 to 12 spike proteins which increases its ease of attachement to the ACE-2 protein [8]. The virus follows an unusual double step replication mechanism thereby growing at an uncontrollable rate [9]. Though the infection has been contained in China, there are a lot of countries at the early stage of the outbreak making them vunnerable to the pandemic. As the incubation period is noted to be 2 to 14 days, the infected person will not have serious symptoms, rather showing commen flu like symptoms including fever, dry cough and breathlessness [10]. As there is no vaccine found to this virus till date, it is very much necessary to control the transmissible rate by alternative means [11]. Quantitative COVID-19 pandemic impact analysis is very scarce in literature and has to be considered seriously given the seriousness of the infection. Epidemics are assumed to have an exponential growth at an early stage and the number of infections over time goes down due to the interventions taken by the government of countries. Mathematical modelling incorporating various precaution measure taken during the viral outbreak using exponential growth analysis coupled with machine learning will give a better prediction model to control COVID-19 transmission [12–14]. Policy changes in pandemic and epidemic situations involve social distancing, lockdowns, travel restrictions, awareness campaigns etc. It is also proven in past research that environmental conditions of countries like temperature and humidity also sometimes play a significant role in controlling pandemics [15].
The objective of the research is twofold an involves data collected from 42 countries. First, it seeks to understand the countries that show an early sign of containment of the COVID-19 virus. Secondly, the research aims at building supervised machine learning models with high accuracies for predicting signs of early continment with infrastructure availability, environmental factors, infection seviority factos, and government policies of countries as independent variables. This report will also involve a discussion on other activities taken by the government of various nations as an attempt to flatten the infections curve and their corresponding effectiveness.
Theory
The COVID-19 origin was linked to Wuhan, China’s animal meat market and it is assumed to be transmitted from bats. This slowly spread all across the world from humans to humans through fluids and aerosol particles. In the initial stages, all diagnozed cases outside china had a travel history to the Wuhan market. However, at later stage, in countries like Itally, US, UK, Korea, Japan etc, community transfers of the disease began infecting people exponentially over time. The nature of the SARS-COV-2 virus bearing the ability to double replicate with the spike protein challenges researchers in vaccines development [3]. Few researchers claim Hydroxychloroquine and azithromycin can be used to treat COVID-19 but not much clinical trials were made to confirm the claim [7]. Hence, until a date arises when a complete cure for the virus is announces, the fate of nations now lie in the hands of epidemiologists to predict spreading patters so that policy makers can take appropriate measures to contain the infection. Several viruses including SARS have reported to be vunnerable to hot temperatures making spreading patterns different based on geographical locations [16]. However, this has not been tested for the COVID-19. Other factors like government policies and interventions, infrastructure availability, and the serveiority of the infection itself can affect the ability of a country to contain epidemics and pandemics. This research seeks to explore all the above factors.
Social Distancing
Social distancing, though a new terminology for the 21st Century, is an older process used by United Kingdom during 1912 to control the Influenza Virus outbreak that caused about a 100 million casualties. Social distancing involves avoiding mass gathering and isolation from an unaffected person with a minimum of 6 feet and is generally combined with enhanced personal hygiene through regular hand wash, and wearing a protective mask for flu like outbreaks [17][18]. This is done primarily because flu causing viruses are generally spread through aerosol that can be transmitted through saliva and nasal fluid at a distance of 3 feet. The average lifetime of COVID-19 viruses in the outer environment is predicted to be 12 hours which makes the transmissibility even higher as aerosol of infected persons on doorknobs, lifts, transports, hotels, malls etc. can widen the outbreak. As it is a communicable disease, it transfers more through greeting the people by shaking hands. Reducing social contact is proven to significantly reduce flu like diseases [19]. The closure of schools and malls flattened the infection during the influenza pandemic in 2009 [20][21]. Governments hence for the COVID-19 case, stress on social distancing and quarantining measures for a period of atleast 14 days to contain the spread of the virus as it is its incubation period [22][23].
Lockdowns
Lockdown is a preventive strategy taken by local or central or global administration during the spread of epidemic or pandemic diseases by closing transportation between cities or provinces or counties. Pandemic is when the spread of the disease crosses countries and international borders rather than only a local region or nearby country. The world has so far seen four pandemics namely plague in the 14thcentury, Infulenza in 1912, SARS in 2009, and the current COVID-19 in 2019 as reported by WHO. [24]The Guardian 2020)[26]. In all these cases, lockdowns were employed by various countries to control the outbreaks. China announce lockdown in the past few months to flatten the exponential curve of the COVID-19 infections over time. Most of the countries around the globe went on a lockdown of local transport, office, industries, city and national borders following china to contain the virus [27]. Though quarantine centers for the infected are available in hospitals, when the infections are huge and uncontrollable spreading happens, the only way to contain infections will be a lockdown where people are self-quarantined at the comfort of their homes [28].
Environment
The environmental conditions of countries like temperature and humidity play an inevitable role in both airborne and aerosol virus transmissions. The 30 year human relationship with the influenza virus has proven that the mortality rate is directly related to the temperature and humidity [29]. Hence, inorder to minimize transmission of diseases, isolation wards in hospitals generally tend to have optimized pressure, temperature, and humidity (WHO, 2014.). The reproduction number R0 was found to be between 2.06 to 2.52 for COVID-19. However, research on the virus in the Diamond Cruise Ship off the coast of Japan has proved that a one degree rise in temperature and a one percent increase in pressure can bring the value down to 0.0383 to 0.0224 though the validity of the study is questionable as the ship was a contained environment [31]. However, the effect of viruses once it enters the human body is not influenced by climatic changes. Since the virus lives outside the human body for a period of atleast 12 hours on normal situations (Richard, 2020), it becomes necessary to study the environmental effects on the spreading patterns itself.
Impact of Health care Infrastructure on transmission
During epidemic and pandemic viral outbreaks health care infrastructure such as availability of hospitals, beds, doctors, clinical equipments, first aid kits, ventilators, and protective equipments plays a vital role in flattening the outburst curve. [33][34]. During the massive influenza outbreak, even developed countries felt the inadequacy in health care infrastructure, which further expands the outbreak (George 2008). The ebola outbreak in South Africa also experienced uncontrollable infections due to lack of infrastructure facilities [36]. After the outbreak, WHO in South Africa had asked the hospitals to report their available facilties to plan for future infections optimally [37]. Certain researches focus on innovative measures to create necessary healthcase infrastructure during pandemic and epidemic situations by converting school, college, theatre, stadium as hospitals [38][39]. Sometimes, health care workers supported by NGOs, youth, and volunteers also play a significant role in containing outbreaks (QH Plan, 2018)[41]. Hence studying health care infrastructure availability across countries can predict COVID-19 containment at an early stage.
Predictive Modelling
Understanding spreading patterns and predictive modelling for policy implementations and actions by the government especially when vaccines to control outbreaks are non existant ( Brooks-pollock, & Thompson, 2019). During the onset of any epidemic, it is crucial to use exponential growth models to understand the infection rates and with proper policy implementations and behavioural changes in the susceptible people, over time the slope reduces and the curve flattens [12]. For various other outbreaks like small pox, ebola, SARS, and influenza, mathematical modelling was used to understand the growth [46] [47,48]. Infact, the Center for Disease Control has an exclusive book for analysing disease outbreaks stressing more on the importance of the forthmentioned [49]. In outbreaks, epidemiologists generally use the exponential growth model on the onset of an outbreak and proceed to prediction and classification techniques like regression, decision trees, neural networks deepl learning, etc. to forecast outbreaks. [13,50]. COVID-19 being a very new outbreak, has limited information and researches on modelling and predicting containment [15,23] and this research seeks to integrate crucial variables concerning infrastructure, environment, policies, and sevearity of the disease to predice initial signs of containment using machine learing and exponential growth model. The variables doctors per 1000 population, beds per 1000 population, average temperature , average humidity, days since official lockdown, percentage of lockdown days, total cases per million population, deaths per million population, days since first contact, and percentage of serious cases of infected were used as a part of the predictive model.