Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries

Abstract

Background: The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.

Objective: The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.

Methods: We first used core terms and symptom-related keyword-based methods to extract COVID-19-related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.

Results: Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t₁₉₈=-8.722, P<.001; model 2, t₁₉₈=-5.000, P<.001, model 3, t₁₉₈=-1.882, P=.06; model 4, t₁₉₈=-4.644, P<.001; model 5, t₁₉₈=-4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t₁₉₈=-1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.

Conclusions: Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.

Keywords: COVID 19; SARS-CoV-2; autoregression model; disease surveillance; epidemic forecasting; infectious disease epidemiology; online news; search query; social medial.

Figure 1

Daily time series of new confirmed COVID-19 case counts (NC), the fraction of COVID-19 related microblogs (Mblog), the fraction of COVID-19–related online news articles (News), and numbers of COVID-19–related search queries with the keyword “fever,” “dry cough,” “chest distress,” “pneumonia,” or “coronavirus” in mainland China, except Hubei province, from December 21, 2019 to February 29, 2020.

Figure 2

Daily time series of new confirmed COVID-19 case counts (NC), the fraction of COVID-19 related microblogs (Mblog), the fraction of COVID-19–related online news articles (News), and numbers of COVID-19–related search queries with the keyword “fever,” “dry cough,” “chest distress,” “pneumonia,” or “coronavirus” in Hubei province from December 21, 2019 to February 29, 2020.

Figure 3

(A) Forecasting results for mainland China, except Hubei, between January 19, 2020 and February 29, 2020, during which the daily estimations of our proposed model and baseline models were compared against the daily new confirmed COVID-19 case counts (NC), and (B) the estimation error, defined as the estimated value minus the daily new confirmed COVID-19 case counts.

Figure 4

(A) Forecasting results for Hubei province between January 27, 2020 and February 29, 2020, during which the daily estimations of our proposed model and baseline models were compared against the daily new confirmed COVID-19 case counts (NC), and (B) the estimation error, defined as the estimated value minus the daily new confirmed COVID-19 case counts.