Viral Mitigation: Weak Theoretical Underpinnings

Mitigation measures included primarily lockdowns and masks and, later in the pandemic, mass vaccination. All of them were supposed to eradicate the disease or at least to "flatten the curve.” To stress the need for disease eradication and/or the need for reduced transmission rates, three postulates were put forward by the proponents of the pandemic policy responses. First, it was claimed that the virus poses a high death risk to all age-groups, and so we need policies that will be able to offer protection to all people. This is the first postulate, which I would like to call the "equal vulnerability thesis.” Second, the claim that there is no pre-existing immunity and hence all people are equally susceptible to the virus, which is the "equal susceptibility thesis.” The third postulate is that the coronavirus can be transmitted not only by symptomatic but also by asymptomatic people. This is the "equal infectivity thesis.” These three premises were mistaken, and the pandemic policies, i.e., lockdowns, masks, and mass vaccination, failed to achieve their declared goals, i.e., they did not eradicate the disease and they did not impact on transmission rates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Prospective Approach on Covid-19 Forecasting Using LSTM

The novel Corona virus has been proclaimed as a worldwide pandemic through World Health Organization in the March 2020 has immensely affected the world with its ferocity. By observation, the scientists got to know that it transmits from one human to other by droplets which range from larger respiratory droplets to smaller aerosols or direct contact with an infected person. Its impurity has been assessed to have an incubation time of 6.4 days than a simple reproduction amount of 2.24-3.58.[19] The transmission rate and spread of infection is quite rapid as compared to other fatal viral infections encountered till date. A massive loss of human life was faced even by the developed countries which had the best health-care facilities. According to WHO, COVID-19 has been confirmed in 238,521,855 people over the world, with 4,863,818 deaths as of October 9th, 2021. After experiencing the second covid wave, the number of cases had got dropped drastically but the increase in their number in the recent days is a major cause of concern. This stresses us to build some prediction models which could help in providing relief to the virus-prone areas. In this study, we are using time series for predicting forthcoming cases of corona virus. © 2022 IEEE.

A Real-Time Social Distancing and Face Mask Detection System Using Deep Learning

It has been more than two years since the transmission of COVID-19 virus has affected the public health globally. Due to its natural characteristic, the virus is very likely to undergo mutation over time and consistently changes to a new variant with higher severity and transmission rate. The pandemic is expected to prolong with the increment in number of daily cases which leads to why preventive measures like practising distance apart rule and wearing facemask are still mandatory in the long run. This paper is prepared to develop a social distancing model using deep learning for COVID-19 pandemic. The tracking accuracy of the proposed model is discussed in the paper and compared with other deep learning methods as well. The efficiency of the detection model is observed and evaluated by performing quantitative metrics. The monitoring model is trained by implementing YOLOv4 algorithm and has achieved an accuracy of 93.79% with F1-score of 0.87 in detecting person and facemask. The model is applicable for real-time and video detection to monitor social distance violation as an effort to flatten the curve and slow down the transmission rate in the community. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data.

Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread.

Interacting With COVID-19: How Population Behavior, Feedback and Memory Shaped Recurrent Waves of the Epidemic

Until the approval of vaccines at the end of 2020, societies relied on non-pharmaceutical interventions (NPIs) in order to control the COVID-19 pandemic. Spontaneous changes in individual behavior might have contributed to or counteracted epidemic control due to NPIs. For example, the population compliance to NPIs may have varied over time as people developed 'epidemic fatigue' or altered their perception of the risk and severity of COVID-19. Whereas official measures are well documented, the behavioral response of the citizens is harder to capture. We propose a mathematical model of the societal response, taking into account three main effects: the citizen response dynamics, the authorities' NPIs, and the occurrence of unpreventable events that significantly alter the virus transmission rate. A key assumption is that a society has a waning memory of the epidemic effects, which reflects on both the severity of the authorities' NPIs and on the citizens' compliance to the prescribed rules. This, in turn, feeds back onto the transmission rate of the disease, such that a higher number of hospitalizations decreases the probability of transmission. We show that the model is able to reproduce the COVID-19 dynamics in terms of hospital admissions for several European countries during 2020 over surprisingly long time scales. Also, it is capable of capturing the effects of disturbances (for example the emergence of new virus variants) and can be exploited for implementing control actions to limit such effects. A possible application, illustrated in this letter, consists of exploiting the estimations based on the data of one country, to predict and control the evolution in another country, where the virus spreading is still in an earlier phase. © 2017 IEEE.

Lessons Learned from Delta and Omicron Variants Transmissions Leveraging Clustering Approach by Sub-Districts in Jakarta

Various variants of COVID-19 have entered Indonesia, such as the delta and the omicron variants. The delta variant has a higher severity than the omicron variant, but the transmission rate for the omicron variant is much faster. The government encourages citizens to get booster vaccines to reduce the effect of the delta and omicron variants. The booster vaccine produced a better effect on citizens than on people who received only the two doses. Therefore, in this study, we observe the transmission of COVID-19 and the vaccine locations on the sub-districts level using the clustering approach. The data we use are COVID-19 positive cases, died, treated, and self-isolated cases. Meanwhile, the vaccination data are 1st dose, 2nd doses, stage 3 of 1st dose, and stage 3 of 2nd doses. The Dunn Index and Hubert Index methods determined the best number of clusters before the clustering process. Silhouette and Davies Bouldin are used to find better clustering between Fuzzy C-Means, K-Means, and Partition Around Medoids (PAM). The results obtained from this study showed that the K-Means method was the best with the best number of clusters, namely 3. Jagakarsa and Kebon Jeruk entered high levels at the time of the delta variant due to the COVID-19 case and vaccination spread. However, Jagakarsa and Kebon Jeruk dropped to the intermediate level during the omicron variant. The benefit of this study is to help the government pay more attention to high COVID-19 cases and low vaccine distribution. © 2022 IEEE.

Investigating the Risk of Airborne COVID-19 and the Importance of Social Restrictions in Regulating the Transmission

COVID-19 is symptomized with a great downfall in the proper functioning of the respiratory system of an affected human. The present work revolves around the research made to study the number of people infected with time under several circumstances. These circumstances include the lockdown introduced by the ruling governments. Additionally, we have shown a pattern for the infected people when the disease can be transmitted through airborne mode. In the present work, we intend to consider a transmission rate that is imparted, to the existing rate, by the airborne nature of the coronavirus. This adds novelty to our present work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Feedback SAIR Model for the Spread of Infectious Disease with Application to COVID-19 Pandemic

The COVID-19 global pandemic has highlighted the importance of identifying effective ways to control the spread of an infectious disease in a population. A solid understanding of the dynamics and the underlying mechanisms that govern this spread is an important step toward such a goal. Susceptible-Asymptomatic-Infected-Recovered (SAIR) models and their variants have played an important role in providing such insight. However, these models have limited explanatory and predictive power due to policy and behavior changes over time. In this paper we introduce a feedback version of the SAIR model by introducing feedback in the disease transmission rate. We apply this model to publicly available COVID-19 infection data. We show this model better captures the dynamics of the disease spread and has much better explanatory and predictive power. Our analysis suggests that public health policies based on daily infection numbers can be more effective than policies based on estimations of infection levels. © 2022 American Automatic Control Council.

Minimizing the infected peak utilizing a single lockdown: a technical result regarding equal peaks

Due to the usage of social distancing as a means to control the spread of the novel coronavirus disease COVID-19, there has been a large amount of research into the dynamics of epidemiological models with time-varying transmission rates. Such studies attempt to capture population responses to differing levels of social distancing, and are used for designing policies which both inhibit disease spread but also allow for limited economic activity. One common criterion utilized for the recent pandemic is the peak of the infected population, a measure of the strain placed upon the health care system;protocols which reduce this peak are commonly said to 'flatten the curve."In this work, we consider a very specialized distancing mandate, which consists of one period of fixed length of distancing, and address the question of optimal initiation time. We prove rigorously that this time is characterized by an equal peaks phenomenon: the optimal protocol will experience a rebound in the infected peak after distancing is relaxed, which is equal in size to the peak when distancing is commenced. In the case of a non-perfect lockdown (i.e. disease transmission is not completely suppressed), explicit formulas for the initiation time cannot be computed, but implicit relations are provided which can be pre-computed given the current state of the epidemic. Expected extensions to more general distancing policies are also hypothesized, which suggest designs for the optimal timing of non-overlapping lockdowns. © 2022 American Automatic Control Council.

Estimation of Some Epidemiological Parameters With the COVID-19 Data of Mayotte

We study in this article some statistical methods to fit some epidemiological parameters. We first consider a fit of the probability distribution which underlines the serial interval distribution of the COVID-19 on a given set of data collected on the viral shedding in patients with laboratory-confirmed. The best-fit model of the non negative serial interval distribution is given by a mixture of two Gamma distributions with different shapes and rates. Thus, we propose a modified version of the generation time function of the package R0. Second, we estimate the time-varying reproduction number in Mayotte. Using a justified mathematical learning model, we estimate the transmission parameters range values during the outbreak together with a sensitivity analysis. Finally, using some regression and forecasting methods, we give some learning models of the hospitalized, intensive care, and death cases over a given period. We end with a discussion and the limit of this study together with some forthcoming theoretical developments.

Research on Improved SEIR Model Predicting UK Coronavirus Variant Omicron Outbreak

As it is reported, the detailed COVID-19 cases have exceeded 400 million worldwide. And there is another outbreak of the COVID-19 infection in England due to the emergence of a new variant: Omicron. Through implementing distinctive control measures, most vaccination has been accomplished to an expansive levels in this country and is as of now in advance. Due to the popularity of vaccines and the success of anti-epidemic measures, the English government decide to announce the last legal restrictions being lifted by February 24, which means that English will be the first country to declare victory over COVID-19. To judge whether the decision is correct or not, we estimate confirmed cases, death, daily new confirmed cases and trend through modeling and simulating method. Considering the effectiveness of vaccines, we raise a new epidemic model named SEIR-V. Our results appear that if transmission rate increases by 15% compared to the current rate due to unwinding of social distancing conditions, the daily new cases can crest to 200k per day around April 1, 2022. The combination of vaccination and controlled legal restrictions is the key to tackling the emergency of the new variant Omicron epidemic. Considering that the new variant strains increase transmissibility and have high resistance to vaccines, the English government should continue the current epidemic prevention measures to avoid the emergence of a second wave of the epidemic. © 2022 SPIE

Predicting Future, Past, and Misinterpreted COVID-19 Cases Using Bidirectional LSTM Model for Proper Health Monitoring: Advances in Data Analytics

The first coronavirus case was reported in Hubei province of China, and within three months, it affected almost all the countries in the world. under such circumstances, World Health Organization (WHO) declared 2019 novel coronavirus as a global pandemic. Even though its fatality is low, the transmission rate makes it more dangerous. Similar to previous disease outbreaks in the human history, COVID-19 also exhibits certain transmission patterns. Mathematical models can be used to analyze these patterns and forecast the upcoming COVID-19 cases. Such forecasting methods could help governments to take further actions to stop those cases from occurring. Most of the previous studies used past infections to forecast future infections. However, they completely neglected the unreported cases while making predictions. By knowing the initially reported cases, we can understand the dynamics of the epidemic more precisely. In order to capture the transmission dynamics, we proposed a novel deep learning model called a B-LSTM (Bidirectional Long Short-Term Memory) model. In order to recalculate the past or missing infections, we applied a masking technique to our B-LSTM model. Results obtained from our model shows that end point of this pandemic in India will be around next year. However, by November the rate of infections will decrease linearly. In addition to that, we compared the forecasting accuracies of B-LSTM with statistical-based ARIMA and LSTM models. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Matching theory and evidence on Covid-19 using a stochastic network SIR model.

This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under-reporting of infected cases. Empirical evidence on six European countries matches the simulated outcomes once the under-reporting of infected cases is addressed. It is estimated that the number of actual cases could be between 4 to 10 times higher than the reported numbers in October 2020 and declined to 2 to 3 times in April 2021. The calibrated models are used in the counterfactual analyses of the impact of social distancing and vaccination on the epidemic evolution and the timing of early interventions in the United Kingdom and Germany.

Mask Recognition with Computer Vision in the Age of a Pandemic

In the current age of coronavirus, monitoring and enforcing correct mask-wearing regulation in public spaces is of paramount importance. Specifically, there is a need to monitor whether people wear masks and whether they wear them correctly. However, there is a lack of automated systems to recognize correct mask-wearing compliance. In this paper, we propose a computer-vision-based solution to the problem of mask-wearing monitoring. In particular, we propose a convolutional neural network to recognize images of people wearing masks correctly, people wearing masks incorrectly, and people not wearing masks at all. Our proposed model is shown to predict correct mask-wearing practices with over 98% accuracy. The model can be easily deployed as an automated system to screen people entering indoor spaces, and can replace current manual, time-consuming, temperaturescreening practices. Such applications can serve as an important tool to help reduce transmission rates during the current pandemic. © 2020, by the authors. All rights reserved.

COVID-19 Diagnosis Using Machine Learning Techniques

There have been significant revolutions in various fields like medical and education on account of improved technological advancements. Furthermore, there have been numerous cases where Machine Learning has been of great help to healthcare by analyzing data and in decision making. Early diagnosis of Covid will help reduce the transmission rate and prevent an outbreak or slow down its spread. COVID-19 is a pandemic which is spreading really fast, affecting and killing millions around the globe and this needs to be addressed soon. Big data has been growing rapidly and there are many public datasets available related to COVID-19. ML could aid in the detection of the disease to bring the current chaotic situation under control. Various machine learning algorithms have been applied in this paper to build the most accurate model that can analyses symptoms of a person and predict if they are covid positive or not using a dataset from Kaggle. The performance of each model was analyzed according to different scoring metrics like accuracy measures, R squared, Precision, ROC curve and on how long the model took to be trained. It can be inferred from this paper that Decision Tree Classifier surpasses all the other algorithms by 98.29% accuracy. © 2022 IEEE.

Montecarlo Simulation in COVID-19 Infection Scenarios to Forecast Industrial Labor Suspensions in Mexico

In the present investigation, the Monte Carlo simulation was applied to the COVID-19 contagion scenario in Mexico. This to evaluate a possible suspension from work through the use of the deterministic SIR model. The research was developed using a descriptive model, since it is only intended to expose the operation of the system. The random variable considered was the infection rate, also known as the basic reproduction number R0, which fluctuates between 0.5 and 2.5. A dynamic transmission rate and a constant recovery rate were applied in the investigation. An Excel spreadsheet was used, and the data obtained was plotted. The data taken for the simulation was from February 20, 2020, to January 31, 2021. The result reflected a significant difference between the historical data and the data obtained in the simulation, this due to the behavior of the dynamic variables that indicated an approximate error of 6,600,000. It can also be observed that the infected cases obtained from the simulation maintain a positive slope, therefore, there is the possibility that this variable will continue to grow. It is worth mentioning that for there to be a work suspension, it was considered that the average R0 was greater than 1.79, considering this as an intermediate value when industrial work was suspended in Mexico. The result obtained from the average R0 was 1.43, which promises a considerable decrease in infections and in view of the restriction, it was concluded that there is no new work suspension in Mexico due to COVID-19. But considering that R0 is greater than 1, there is latency of infections, therefore, preventive measures must be maintained. © 2021 IEEE.

Study of Electrostatic Field Responses of Locally Marketed Face Masks in the Philippines

The face mask is the first line of defense against infectious particulates and droplets that may cause illness. Currently in the Philippines, the wearing of face mask is compulsory whenever citizens leave their residences as mandated by the government to mitigate the spread of COVID-19. The wearing of face masks has become a new normal among Filipinos. This created market opportunities for different types which became commonly and immediately available for purchase. This study aimed to differentiate the effectiveness of locally available face masks in terms of electrostatic filtration capability. Twelve different types of face masks grouped into five categories – surgical, fabric, N95 variants, foam type, and novelty type – were evaluated. Electrostatic fields were measured from each face mask including pore sizes via scanning electron microscopy. Moreover, by utilizing the estimated charge and mass of the SARS-CoV-2 virion, the transmission rate was simulated using COMSOL Multiphysics®. It was observed that face masks with negatively charged materials combined with small pore sizes afforded less particle transmission. The results of this study are of timely significance in potentially laying out public awareness in the selection and utilization of face masks that can provide foremost shielding against viral transmission. © 2021 Institute of Physics Publishing. All rights reserved.

Modeling Transmission Rate of COVID-19 in Regional Countries to Forecast Newly Infected Cases in a Nation by the Deep Learning Method

This paper presents a deep learning approach to predict new COVID-19 infected cases in a specific country with insufficient data at the onset of the outbreak. We collected data on daily new confirmed cases in several countries of the region where COVID-19 occurred earlier and caused more severe effects than in Vietnam. Then we computed some deep machine learning models to adapt the spreading speed of Delta strain in each nation to generate various scenarios for the epidemic situation in Vietnam. We used models based on recurrent neural networks (RNN) architectures such as long-short term memory (LSTM), gated recurrent unit (GRU), and several hybrid structures between LSTM and GRU. Learning from the experiments in this research, we built a set of circumstances for COVID-19 in Vietnam. We also found that GRU always gives the best performance in terms of MSE, while LSTM is the worst. © 2021, Springer Nature Singapore Pte Ltd.

Maximum Daily Temperature, Precipitation, Ultraviolet Light, and Rates of Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 in the United States.

BACKGROUND: Previous reports have suggested that transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is reduced by higher temperatures and higher humidity. We analyzed case data from the United States to investigate the effects of temperature, precipitation, and ultraviolet (UV) light on community transmission of SARS-CoV-2. METHODS: Daily reported cases of SARS-CoV-2 across the United States from 22 January 2020 to 3 April 2020 were analyzed. We used negative binomial regression modeling to determine whether daily maximum temperature, precipitation, UV index, and the incidence 5 days later were related. RESULTS: A maximum temperature above 52°F on a given day was associated with a lower rate of new cases at 5 days (incidence rate ratio [IRR], 0.85 [0.76, 0.96]; P = .009). Among observations with daily temperatures below 52°F, there was a significant inverse association between the maximum daily temperature and the rate of cases at 5 days (IRR, 0.98 [0.97, 0.99]; P = .001). A 1-unit higher UV index was associated with a lower rate at 5 days (IRR, 0.97 [0.95, 0.99]; P = .004). Precipitation was not associated with a greater rate of cases at 5 days (IRR, 0.98 [0.89, 1.08]; P = .65). CONCLUSIONS: The incidence of disease declines with increasing temperature up to 52°F and is lower at warmer vs cooler temperatures. However, the association between temperature and transmission is small, and transmission is likely to remain high at warmer temperatures.

Cohabitation COVID-19 transmission rates in a United States suburban community: A retrospective study of familial infections.

OBJECTIVES: SARS-CoV-2 is a highly contagious virus that causes coronavirus disease 2019 (COVID-19) and can affect people of any age with potential for serious symptoms. Since the start of the COVID-19 pandemic, global infection rates have been on the rise with world leaders looking to slow and stop viral transmission. This study is looking at suburban cohabitation/familial infection to compare to similar studies from other countries. STUDY DESIGN: A retrospective review of medical records was collected using the Connecticut Electronic Disease Surveillance System. METHODS: A total of 406 cases who tested positive for SARS-COV-2 from February to June 2020 were reviewed from three towns located in Connecticut, USA. Cohabitation infection rates were identified using the home addresses of those with confirmed SARS-CoV-2 test results, with the first documented case being the index case, and additional home members being the secondary cases. RESULTS: Secondary transmission of SARS-CoV-2 developed in 126 of 406 household contacts (31%). Linear regression indicated positive relationship between cohabitation and age. CONCLUSIONS: The cohabitation infection attack rate of SARS-CoV-2 is significantly higher than previously reported. Age of household contacts and spousal relationship to the index case are risk factors for transmission of SARS-CoV-2 within a household.