Spatial and Temporal Distributions of Air Pollutants in Nanchang, Southeast China during 2017–2020

In response to COVID-19 in December 2019, China imposed a strict lockdown for the following two months, which led to an unprecedented reduction in industrial activities and transportation. However, haze pollution was still recorded in many Chinese cities during the lockdown period. To explore temporal and spatial variations in urban haze pollution, concentrations of air pollutants (PM2.5, PM10, SO2, CO, NO, NO2, and O3) from April 2017 to March 2020 were observed at 23 monitoring stations throughout Nanchang City (including one industrial site, sixteen urban central sites, two mountain sites, and four suburban sites). Overall, the highest concentrations of PM2.5, PM10, and SO2 were observed at industrial sites and the highest CO and NOx (NO and NO2) concentrations were recorded at urban sites. The air pollutants at mountain sites all showed the lowest concentrations, which indicated that anthropogenic activities are largely responsible for air pollutants. Concentrations of PM2.5, PM10, CO, NO, and NO2 showed similar season trends, that is, the highest levels in winter and lowest concentrations in summer, but an opposite season pattern for O3. Except for a sharply dropping pattern from January to May 2018, there were no seasonal patterns for SO2 concentration in all the observed sites. Daily PM2.5, PM10, CO, NOx, and SO2 concentrations showed a peak during the morning commute, which indicated the influences of anthropogenic activities on PM2.5, PM10, CO, NOx, and SO2. PM2.5, PM10, NOx, and CO concentrations at industrial, urban, and suburban sites were higher during nighttime than during daytime, but they showed the opposite pattern at mountain sites. In addition, PM2.5, PM10, CO, and NOx concentrations were lower during the lockdown period (D2) than those before the lockdown (B1). After the lockdown was lifted (A3), PM2.5, PM10, CO, and NOx concentrations showed a slowly increasing trend. However, O3 concentrations continuously increased from B1 to A3.

Recurrent Neural Reinforcement Learning for Counterfactual Evaluation of Public Health Interventions on the Spread of Covid-19 in the world (preprint)

As the Covid-19 pandemic soars around the world, there is urgent need to forecast the expected number of cases worldwide and the length of the pandemic before receding and implement public health interventions for significantly stopping the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid-19 are epidemiological models. Although these epidemiological models are useful for estimating the dynamics of transmission of epidemics, their prediction accuracies are quite low. Alternative to the epidemiological models, the reinforcement learning (RL) and causal inference emerge as a powerful tool to select optimal interventions for worldwide containment of Covid-19. Therefore, we formulated real-time forecasting and evaluation of multiple public health intervention problems into off-policy evaluation (OPE) and counterfactual outcome forecasting problems and integrated RL and recurrent neural network (RNN) for exploring public health intervention strategies to slow down the spread of Covid-19 worldwide, given the historical data that may have been generated by different public health intervention policies. We applied the developed methods to real data collected from January 22, 2020 to June 28, 2020 for real-time forecasting the confirmed cases of Covid-19 across the world. We forecasted that the number of laboratory confirmed cumulative cases of Covid-19 will pass 26 million as of August 14, 2020.

Spread of Covid-19 in the United States is controlled (preprint)

As of May 1, 2020, the number of cases of Covid-19 in the US passed 1,062,446, interventions to slow down the spread of Covid-19 curtailed most social activities. Meanwhile, an economic crisis and resistance to the strict intervention measures are rising. Some researchers proposed intermittent social distancing that may drive the outbreak of Covid-19 into 2022. Questions arise about whether we should maintain or relax quarantine measures. We developed novel artificial intelligence and causal inference integrated methods for real-time prediction and control of nonlinear epidemic systems. We estimated that the peak time of the Covid-19 in the US would be April 24, 2020 and its outbreak in the US will be over by the end of July and reach 1,551,901 cases. We evaluated the impact of relaxing the current interventions for reopening economy on the spread of Covid-19. We provide tools for balancing the risks of workers and reopening economy.

A Noel Intervention Recurrent autoencoder for real time forecasting and non-pharmaceutical intervention selection to curb the spread of Covid-19 in the world (preprint)

As the Covid-19 pandemic soars around the world, there is urgent need to forecast the number of cases worldwide at its peak, the length of the pandemic before receding and implement public health interventions to significantly stop the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid-19 are epidemiological models. Although these epidemiological models are useful for estimating the dynamics of transmission od epidemics, their prediction accuracies are quite low. To overcome this limitation, we formulated the real-time forecasting and evaluating multiple public health intervention problem into forecasting treatment response problem and developed recurrent neural network (RNN) for modeling the transmission dynamics of the epidemics and Counterfactual-RNN (CRNN) for evaluating and exploring public health intervention strategies to slow down the spread of Covid-19 worldwide. We applied the developed methods to the real data collected from January 22, 2020 to May 8, 2020 for real-time forecasting the confirmed cases of Covid-19 across the world.

Forecasting and evaluating intervention of Covid-19 in the World (preprint)

When the Covid-19 pandemic enters dangerous new phase, whether and when to take aggressive public health interventions to slow down the spread of COVID-19. To develop the artificial intelligence (AI) inspired methods for real-time forecasting and evaluating intervention strategies to curb the spread of Covid-19 in the World. A modified auto-encoder for modeling the transmission dynamics of the epidemics is developed and applied to the surveillance data of cumulative and new Covid-19 cases and deaths from WHO, as of March 16, 2020. The average errors of 5-step forecasting were 2.5%. The total peak number of cumulative cases and new cases, and the maximum number of cumulative cases in the world with later intervention (comprehensive public health intervention is implemented 4 weeks later) could reach 75,249,909, 10,086,085, and 255,392,154, respectively. The case ending time was January 10, 2021. However, the total peak number of cumulative cases and new cases and the maximum number of cumulative cases in the world with one week later intervention were reduced to 951,799, 108,853 and 1,530,276, respectively. Duration time of the Covid-19 spread would be reduced from 356 days to 232 days. The case ending time was September 8, 2020. We observed that delaying intervention for one month caused the maximum number of cumulative cases to increase 166.89 times, and the number of deaths increase from 53,560 to 8,938,725. We will face disastrous consequences if immediate action to intervene is not taken.

Evaluating the effect of public health intervention on the global-wide spread trajectory of Covid-19 (preprint)

As COVID-19 evolves rapidly, the issues the governments of affected countries facing are whether and when to take public health interventions and what levels of strictness of these interventions should be, as well as when the COVID-19 spread reaches the stopping point after interventions are taken. To help governments with policy-making, we developed modified auto-encoders (MAE) method to forecast spread trajectory of Covid-19 of countries affected, under different levels and timing of intervention strategies. Our analysis showed public health interventions should be executed as soon as possible. Delaying intervention 4 weeks after March 8, 2020 would cause the maximum number of cumulative cases of death increase from 7,174 to 133,608 and the ending points of the epidemic postponed from Jun 25 to Aug 22.

Artificial Intelligence Forecasting of Covid-19 in China (preprint)

BACKGROUND An alternative to epidemiological models for transmission dynamics of Covid-19 in China, we propose the artificial intelligence (AI)-inspired methods for real-time forecasting of Covid-19 to estimate the size, lengths and ending time of Covid-19 across China. METHODS We developed a modified stacked auto-encoder for modeling the transmission dynamics of the epidemics. We applied this model to real-time forecasting the confirmed cases of Covid-19 across China. The data were collected from January 11 to February 27, 2020 by WHO. We used the latent variables in the auto-encoder and clustering algorithms to group the provinces/cities for investigating the transmission structure. RESULTS We forecasted curves of cumulative confirmed cases of Covid-19 across China from Jan 20, 2020 to April 20, 2020. Using the multiple-step forecasting, the estimated average errors of 6-step, 7-step, 8-step, 9-step and 10-step forecasting were 1.64%, 2.27%, 2.14%, 2.08%, 0.73%, respectively. We predicted that the time points of the provinces/cities entering the plateau of the forecasted transmission dynamic curves varied, ranging from Jan 21 to April 19, 2020. The 34 provinces/cities were grouped into 9 clusters. CONCLUSIONS The accuracy of the AI-based methods for forecasting the trajectory of Covid-19 was high. We predicted that the epidemics of Covid-19 will be over by the middle of April. If the data are reliable and there are no second transmissions, we can accurately forecast the transmission dynamics of the Covid-19 across the provinces/cities in China. The AI-inspired methods are a powerful tool for helping public health planning and policymaking.