Artificial Intelligence Forecasting of Covid-19 in China

Nepal was hard hit by a second wave of COVID-19 from April-May 2021. We investigated the transmission dynamics of COVID-19 at the national and provincial levels by using data on laboratory-confirmed RT-PCR positive cases from the official national situation reports. We performed 8 week-to-week sequential forecasts of 10-days and 20-days at national level using three dynamic phenomenological growth models from 5 March 2021-22 May 2021. We also estimated effective and instantaneous reproduction numbers at national and provincial levels using established methods and evaluated the mobility trends using Google's mobility data. Our forecast estimates indicated a declining trend of COVID-19 cases in Nepal as of June 2021. Sub-epidemic and Richards models provided reasonable short-term projections of COVID-19 cases based on standard performance metrics. There was a linear pattern in the trajectory of COVID-19 incidence during the first wave (deceleration of growth parameter (p) = 0.41-0.43, reproduction number (Rt) at 1.1 (95% CI: 1.1, 1.2)), and a sub-exponential growth pattern in the second wave (p = 0.61 (95% CI: 0.58, 0.64)) and Rt at 1.3 (95% CI: 1.3, 1.3)). Across provinces, Rt ranged from 1.2 to 1.5 during the early growth phase of the second wave. The instantaneous Rt fluctuated around 1.0 since January 2021 indicating well sustained transmission. The peak in mobility across different areas coincided with an increasing incidence trend of COVID-19. In conclusion, we found that the sub-epidemic and Richards models yielded reasonable short-terms projections of the COVID-19 trajectory in Nepal, which are useful for healthcare utilization planning.

Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.

The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, beta_{t} and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.

<div> <div> <div> <div> <div> <div> <div> <div> <div> <div>The World Meteorological Organization (WMO) Research Board has set up an interdisciplinary and international Task Team to respond to the challenge of providing timely decision support and relevant knowledge on Meteorological and Air Quality (MAQ) factors affecting the SARS-CoV-2/COVID-19 pandemic. The Task Team aims to provide decision makers and the public with a rapid summary of the state of knowledge regarding potential MAQ influences on SARS-CoV-2/COVID-19; to offer general technical guidance for researchers and service providers who wish to consider MAQ data in their analyses, estimates, predictions and projections of COVID-19 risks. The work of the task  motivated both by the global relevance of the subject and by the staggering number of papers and pre-prints currently available, which emphasizes the need for careful review and communication of the state of the science. This first  report presents a summary of key findings of the review to date, as informed by peer reviewed literature.</div> <p> </p> <div>A key finding is that the underlying mechanisms that drive seasonality of respiratory viral infections are not yet well understood. To date, COVID-19 transmission dynamics appear to have been controlled primarily by government interventions rather than meteorological factors. Respiratory viral infections frequently exhibit some form of seasonality, particularly in temperate climates and some evidence from laboratory studies of SARS-CoV-2, suggests that the virus survives longer under cold, dry, and low ultraviolet radiation conditions. There is also evidence that chronic and short-term exposure to air pollution exacerbates symptoms and increases mortality rates for some respiratory diseases and this is consistent with early studies of COVID-19 mortality rates. However, there is no direct, peer reviewed evidence of pollution impacts on the transmission of SARS-CoV-2 at this time. Process-based modeling studies anticipate that COVID-19 transmission may become seasonal over time, suggesting Meteorology and Air Quality (MAQ) factors may support monitoring and forecasting of COVID-19 in the coming months and years.</div> <p> </p> <div>Additional research quantifying links between MAQ factors and COVID-19 is needed.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div>

Coronavirus Disease 2019 (COVID-19) is a zoonotic illness which has spread rapidly and widely in past two years and was identified as a global pandemic by the World Health Organization (WHO). The pandemic to date has been characterized by ongoing cluster community transmission. Quarantine intervention to prevent and control the transmission is expected to have a substantial impact on delaying the growth and mitigating the size of the epidemic. To our best knowledge, this study is among the initial efforts to analyze the interplay between transmission dynamics and quarantine intervention of the COVID-19 outbreak in a cluster community. In the chapter, we propose a novel Transmission-Quarantine epidemiological model by non-linear ordinary differential equations system. With the use of detailed epidemiologic data from the Cruise ship “Diamond Princess,” we design a Transmission-Quarantine work-flow to determine the optimal case-specific parameters and validate the proposed model by comparing the simulated curve with the real data. Firstly, we apply a general SEIR-type epidemic model to study the transmission dynamics of COVID-19 without quarantine intervention and present the analytic and simulation results for the epidemiological parameters such as the basic reproduction number, the maximal scale of infectious cases, the instant number of recovered cases, the popularity level, and the final scope of the epidemic of COVID-19. Secondly, we adopt the proposed Transmission-Quarantine interplay model to predict the varying trend of COVID-19 with quarantine intervention and compare the transmission dynamics with and without quarantine to illustrate the effectiveness of the quarantine measure, which indicates that with quarantine intervention, the number of infectious cases in 7 days decreases by about 60%, compared with the scenario of no intervention. Finally, we conduct sensitivity analysis to simulate the impacts of different parameters and different quarantine measures and identify the optimal quarantine strategy that can be used by the decision makers to achieve the maximal protection of population with the minimal interruption of economic and social development.

The COVID-19 outbreak has caused two waves and spread to more than 90% of Canada’s provinces since it was first reported more than a year ago. During the COVID-19 epidemic, Canadian provinces have implemented many Non-Pharmaceutical Interventions (NPIs). However, the spread of the COVID-19 epidemic continues due to the complex dynamics of human mobility. We develop a meta-population network model to study the transmission dynamics of COVID-19. The model takes into account the heterogeneity of mitigation strategies in different provinces of Canada, such as the timing of implementing NPIs, the human mobility in retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residences due to work and recreation. To determine which activity is most closely related to the dynamics of COVID-19, we use the cross-correlation analysis to find that the positive correlation is the highest between the mobility data of parks and the weekly number of confirmed COVID-19 from February 15 to December 13, 2020. The average effective reproduction numbers in nine Canadian provinces are all greater than one during the time period, and NPIs have little impact on the dynamics of COVID-19 epidemics in Ontario and Saskatchewan. After November 20, 2020, the average infection probability in Alberta became the highest since the start of the COVID-19 epidemic in Canada. We also observe that human activities around residences do not contribute much to the spread of the COVID-19 epidemic. The simulation results indicate that social distancing and constricting human mobility is effective in mitigating COVID-19 transmission in Canada. Our findings can provide guidance for public health authorities in projecting the effectiveness of future NPIs.

We introduce two mathematical models based on systems of differential equations to investigate the relationship between the latency period and the transmission dynamics of COVID-19. We analyze the equilibrium and stability properties of these models, and perform an asymptotic study in terms of small and large latency periods. We fit the models to the COVID-19 data in the U.S. state of Tennessee. Our numerical results demonstrate the impact of the latency period on the dynamical behaviors of the solutions, on the value of the basic reproduction numbers, and on the accuracy of the model predictions.

Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil.Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented.Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%.Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America.

The data on SARS-CoV-2 (COVID-19) in South Africa show seasonal transmission patterns to date, with the peaks having occurred in winter and summer since the outbreaks began. The transmission dynamics have mainly been driven by variations in environmental factors and virus evolution, and the two are at the center of driving the different waves of the disease. It is thus important to understand the role of seasonality in the transmission dynamics of COVID-19. In this paper, a compartmental model with a time-dependent transmission rate is formulated and the stabilities of the steady states analyzed. We note that if R 0 < 1 , the disease-free equilibrium is globally asymptotically stable, and the disease completely dies out; and when R 0 > 1 , the system admits a positive periodic solution, and the disease is uniformly or periodically persistent. The model is fitted to data on new cases in South Africa for the first four waves. The model results indicate the need to consider seasonality in the transmission dynamics of COVID-19 and its importance in modeling fluctuations in the data for new cases. The potential impact of seasonality in the transmission patterns of COVID-19 and the public health implications is discussed.

Effect of vaccination on the transmission dynamics of COVID-19 in Ethiopia

Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections

The complexities involved in modelling the transmission dynamics of COVID-19 has been a roadblock in achieving predictability in the spread and containment of the disease. In addition to understanding the modes of transmission, the effectiveness of the mitigation methods also needs to be built into any effective model for making such predictions. We show that such complexities can be circumvented by appealing to scaling principles which lead to the emergence of universality in the transmission dynamics of the disease. The ensuing data collapse renders the transmission dynamics largely independent of geopolitical variations, the effectiveness of various mitigation strategies, population demographics, etc. We propose a simple two-parameter model—the Blue Sky model—and show that one class of transmission dynamics can be explained by a solution that lives at the edge of a blue sky bifurcation. In addition, the data collapse leads to an enhanced degree of predictability in the disease spread for several geographical scales which can also be realized in a model-independent manner as we show using a deep neural network. The methodology adopted in this work can potentially be applied to the transmission of other infectious diseases and new universality classes may be found. The predictability in transmission dynamics and the simplicity of our methodology can help in building policies for exit strategies and mitigation methods during a pandemic.

The collaboration between infectious disease modeling and public health decision-making based on the COVID-19

Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review

The world is facing several challenges due to the COVID-19 pandemic, which is causing severe social and economic disruption. The disruption has resulted in recession, unemployment, and social isolation and has caused an extreme burden on health care services. Artificial intelligence (AI) has a promising role in the healthcare sector by bringing many advantages to practicing clinicians, patients, and society. This article aims to determine the role of artificial intelligence in the ongoing COVID-19 pandemic. A quantitative methodology is used, and a literature review is done by using electronic databases such as PubMed, Google Scholar, and Scopus. The keywords used for this data research are "artificial intelligence" and "COVID 19"; "COVID 19" and "artificial intelligence". The results have shown that artificial intelligence has been extensively used in seven major domains during the current ongoing COVID-19 pandemic. These include screening and detection of COVID-19 transmission dynamics, diagnostics, disease monitoring and forecasting, disease outbreak containment, disease recovery and mortality, treatment and vaccination, and protection of healthcare workers. Artificial intelligence is a transformational force in the medical field. It assists in early detection of disease, real-time surveillance, diagnosis, treatment, disease containment, development of treatment and vaccinations, and reducing morbidity and mortality. Artificial intelligence will help us in the future to meet many challenges in a timely fashion through the prediction of pandemics, making stakeholders worldwide well prepared to deal with epidemics and pandemics in a systematic and organized manner, avoiding economic turmoil and unnecessary morbidity and mortality.