A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread

Abstract

We have developed a globally applicable diagnostic COVID-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon previous epidemics like SARS/MERS and all parameters are optimized via machine learning algorithms used on publicly available COVID-19 data. The model decomposes the contributions to the infection time series to analyze and compare the role of quarantine control policies used in highly affected regions of Europe, North America, South America, and Asia in controlling the spread of the virus. For all continents considered, our results show a generally strong correlation between strengthening of the quarantine controls as learnt by the model and actions taken by the regions' respective governments. In addition, we have hosted our quarantine diagnosis results for the top 70 affected countries worldwide, on a public platform.

Keywords: COVID-19; epidemiology; machine learning.

Graphical abstract

Figure 1

Illustration of the QSIR Model and Neural Network Architecture (A) Schematic of the augmented QSIR model considered in the present study. (B) Schematic of the neural network architecture used to learn the quarantine strength function $Q\left(t\right)$. Here, $T\left(t\right)$ represents the quarantined infected population prescribed by the quarantine strength rate $Q\left(t\right)$.

Figure 2

Europe: Infected and Recovered COVID-19 Case Count Evolution COVID-19 infected and recovered evolution compared with our neural network augmented model prediction in the highest affected European countries as of June 1, 2020.

Figure 3

Europe: Quarantine Strength Evolution in Response to COVID-19 Quarantine strength $Q\left(t\right)$ learnt by the neural network in the highest affected European countries as of June 1, 2020. The transition from the red to blue shaded regions indicates the COVID-19 spread parameter of value $C_p<1$ leading to halting of the infection spread. The green dashed line indicates the time when quarantine measures were implemented in the region under consideration, which generally matches well with an inflection point seen in the $Q\left(t\right)$ plot denoted by the red dashed line. For regions in which a clear inflection or ramp-up point is not seen (Russia), the red dashed line is not shown.

Figure 4

Europe: COVID-19 Spread Parameter Evolution in Response to COVID-19 Control of COVID-19 quantified by the COVID-19 spread parameter evolution in the highest affected European countries as of June 1, 2020. The transition from the red to blue shaded regions indicates $C_p<1$ leading to halting of the infection spread.

Figure 5

Europe: Quarantine Efficiency Heatmap Quarantine efficiency, $Q_{\text{eff}}$ defined in (Equation 12) for the 23 highest affected European countries. Note that $Q_{\text{eff}}$ is indicative of the quarantine responsiveness: the testing and tracing protocols to identify and isolate infected individuals. The map also shows the demarcation between countries with a high $Q_{\text{eff}}$ shown by a green dotted line and those with a low $Q_{\text{eff}}$ shown by a red dotted line.

Figure 6

USA: Infected and Recovered COVID-19 Case Count Evolution COVID-19 infected and recovered evolution compared with our neural network augmented model prediction in the highest affected USA states as of June 1, 2020.

Figure 7

USA: Quarantine Strength Evolution in Response to COVID-19 Quarantine strength $Q\left(t\right)$ learnt by the neural network in the highest affected USA states as of June 1, 2020. The transition from the red to blue shaded regions indicates the COVID-19 spread parameter of value $C_p<1$ leading to halting of the infection spread. The green dashed line indicates the time when quarantine measures were implemented in the region under consideration, which generally matches well with an inflection point (for New York, New Jersey, and Illinois) or a ramp-up point (California) seen in the $Q\left(t\right)$ plot denoted by the red dashed line.

Figure 8

USA: COVID-19 Spread Parameter Evolution in Response to COVID-19 Control of COVID-19 quantified by the COVID-19 spread parameter evolution in the highest affected USA states as of June 1, 2020. The transition from the red to blue shaded regions indicates $C_p<1$ leading to halting of the infection spread.

Figure 9

USA: Quarantine Efficiency Heatmap and Its Comparison with Ground Truth Data (A) Quarantine efficiency, $Q_{\text{eff}}$ defined in (Equation 12) for 20 major USA states. Note that $Q_{eff}$ is indicative of the quarantine responsiveness: the testing and tracing protocols to identify and isolate infected individuals. (B) Comparison between a report published in the Wall Street Journal on May 21 and the quarantine efficiency magnitude in our study. A generally strong correlation is seen between the magnitude of quarantine efficiency in our study and the level of restrictions actually imposed in different USA states.

Figure 10

Asia: Infected and Recovered COVID-19 Case Count Evolution COVID-19 infected and recovered evolution compared with our neural network augmented model prediction in the highest affected Asian countries as of June 1, 2020.

Figure 11

Asia: Quarantine Strength Evolution in Response to COVID-19 Quarantine strength $Q\left(t\right)$ learnt by the neural network in the highest affected Asian countries as of June 1, 2020. The transition from the red to blue shaded regions indicates the COVID-19 spread parameter of value $C_p<1$ leading to halting of the infection spread. The green dashed line indicates the time when quarantine measures were implemented in the region under consideration, which generally matches well with a ramp-up point seen in the $Q\left(t\right)$ plot denoted by the red dashed line. For regions in which a clear inflection or ramp-up point is not seen (India), the red dashed line is not shown.

Figure 12

Asia: COVID-19 Spread Parameter Evolution in Response to COVID-19 Control of COVID-19 quantified by the COVID-19 spread parameter evolution in the highest affected Asian countries as of June 1, 2020. The transition from the red to blue shaded regions indicates $C_p<1$ leading to halting of the infection spread.

Figure 13

South America: Infected and Recovered COVID-19 Case Count Evolution COVID-19 infected and recovered evolution compared with our neural network augmented model prediction in the highest affected South American countries as of June 1, 2020.

Figure 14

South America: Quarantine Strength Evolution in Response to COVID-19 Quarantine strength $Q\left(t\right)$ learnt by the neural network in the highest affected South American countries as of June 1, 2020. The transition from the red to blue shaded regions indicates the COVID-19 spread parameter of value $C_p<1$ leading to halting of the infection spread. The green dotted line indicates the time when quarantine measures were implemented in the region under consideration.

Figure 15

South America: COVID-19 Spread Parameter Evolution in Response to COVID-19 Control of COVID-19 quantified by the COVID-19 spread parameter evolution in the highest affected South American countries as of June 1, 2020. The transition from the red to blue shaded regions indicates $C_p<1$ leading to halting of the infection spread.

Figure 16

COVID-19 Spread and Subsequent Response of Majorly Affected Continents and Countries Therein Global comparison of infection, recovery rates, and quarantine efficiency.

Figure 17

Gaussian Process Residue Regression Model Gaussian process residue model fitted to (A) the infected case count and (B) the recovered case count for Russia.

Figure 18

Parameter Inference to Demonstrate Robustness of QSIR Model Recovered Parameters Inferred parameters for 500 realizations of the Gaussian process residue model superimposed on the best fit model prediction applied to Russia and shown for (A) the quarantine strength function $Q\left(t\right)$, (B) the contact rate β, and the recovery rate $\gamma +\delta$. A total of 30 million iterations were performed on the MIT Supercloud cluster to generate parameter histograms for one country.