摘要：Natural and non‐natural factors have combined effects on the trajectory of COVID‐19 pandemic, but it is difficult to make them separate. To address this problem, a two‐stepped methodology is proposed. First, a compound natural factor (CNF) model is developed via assigning weight to each of seven investigated natural factors, that is temperature, humidity, visibility, wind speed, barometric pressure, aerosol, and vegetation in order to show their coupling relationship with the COVID‐19 trajectory. Onward, the empirical distribution based framework (EDBF) is employed to iteratively optimize the coupling relationship between trajectory and CNF to express the real interaction. In addition, the collected data is considered from the backdate, that is about 23 days—which contains 14‐days incubation period and 9‐days invalid human response time—due to the nonavailability of prior information about the natural spreading of virus without any human intervention(s), and also lag effects of the weather change and social interventions on the observed trajectory due to the COVID‐19 incubation period; Second, the optimized CNF‐plus‐polynomial model is used to predict the future trajectory of COVID‐19. Results revealed that aerosol and visibility show the higher contribution to transmission, wind speed to death, and humidity followed by barometric pressure dominate the recovery rates, respectively. Consequently, the average effect of environmental change to COVID‐19 trajectory in China is minor in all variables, that is about −0.3%, 0.3%, and 0.1%, respectively. In this research, the response analysis of COVID‐19 trajectory to the compound natural interactions presents a new prospect on the part of global pandemic trajectory to environmental changes. Plain Language Abstract The World Health Organization declared COVID‐19 a pandemic on March 11, 2020. NATURE and SCIENCE published articles affirming the positive effect of non‐natural interventions on mitigating the pandemic in China but still the possibility cannot be ruled out that the decrease is partially attributable to other unknown climatic factors. Our work separated the response of COVID‐19 trajectory to natural and non‐natural factors. First, the response of COVID‐19 trajectory to the seven single natural factors (SNFs), that is temperature, humidity, wind speed, aerosol, visibility, barometric pressure, and vegetation are investigated, respectively. Onward, a compound natural factor (CNF) is proposed to draw the combined effect with virus spread. Through assigning optimal weight values to SNFs, a coupling relationship is expressed for the interaction between compound natural factor and COVID‐19 pandemic. As a result, CNF exhibits the sensitive response to COVID‐19 trajectory. With a simple computer code to predict future COVID‐19 trajectory purely driven though natural factors, it is confirmed that reduction in CNF value (outcome of the weather change) could help delay the spread of virus, but increase the death and decrease the recovery. On the contrary, increased CNF value could to some extent decrease the death and increase the recovery, but accelerate the virus spread simultaneously. Modeling results suggest that during the valid human response time, combined with the accessorial natural interaction, the non‐natural factors basically dominated the COVID‐19 pandemic trajectory. However, when the COVID‐19 trajectory entered the retreated phase (e.g., in China and Australia, etc.,), the effect of natural interaction to subsequent trajectory became important.
