Conditions for a Second Wave of COVID-19 Due to Interactions Between Disease Dynamics and Social Processes - Pedro, Sansao A.; Ndjomatchoua, Frank T.; Jentsch, Peter; Tchuenche, Jean M.; Anand, Madhur; Bauch, Chris T. (Frontiers in Physics, 2020)
In May 2020, many jurisdictions around the world began lifting physical distancing restrictions against the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This gave rise to concerns about a possible second wave of coronavirus disease 2019 (COVID-19). These restrictions were imposed in response to the presence of COVID-19 in populations, usually with the broad support of affected populations. However, the lifting of restrictions is also a population response to the accumulating socio-economic impacts of restrictions, and lifting of restrictions is expected to increase the number of COVID-19 cases, in turn. This suggests that the COVID-19 pandemic exemplifies a coupled behaviour-disease system where disease dynamics and social dynamics are locked in a mutual feedback loop. Here we develop a minimal mathematical model of the interaction between social support for school and workplace closure and the transmission dynamics of SARS-CoV-2. We find that a second wave of COVID-19 occurs across a broad range of plausible model input parameters governing epidemiological and social conditions, on account of instabilities generated by behaviour-disease interactions. The second wave tends to have a higher peak than the first wave when the efficacy of restrictions is greater than 40% and when the basic reproduction number R_0 is less than 2.4. Surprisingly, we also found that a lower R_0 value makes a second wave more likely, on account of behavioural feedback (although a lower R_0 does not necessarily cause more infections, in total). We conclude that second waves of COVID-19 can be interpreted as the outcome of nonlinear interactions between disease dynamics and social behaviour. We also suggest that further development of mathematical models exploring behaviour-disease interactions could help us better understand how social and epidemiological conditions together determine how pandemics unfold.