A Tale of Three Recent Pandemics: Influenza, HIV and SARS-CoV-2

Emerging infectious diseases are one of the main threats to public health, with the potential to cause a pandemic when the infectious agent manages to spread globally. The first major pandemic to appear in the 20th century was the influenza pandemic of 1918, caused by the influenza A H1N1 strain that is characterized by a high fatality rate. Another major pandemic was caused by the human immunodeficiency virus (HIV), that started early in the 20th century and remained undetected until 1981. The ongoing HIV pandemic demonstrated a high mortality and morbidity rate, with discrepant impacts in different regions around the globe. The most recent major pandemic event, is the ongoing pandemic of COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has caused over 5.7 million deaths since its emergence, 2 years ago. The aim of this work is to highlight the main determinants of the emergence, epidemic response and available countermeasures of these three pandemics, as we argue that such knowledge is paramount to prepare for the next pandemic. We analyse these pandemics' historical and epidemiological contexts and the determinants of their emergence. Furthermore, we compare pharmaceutical and non-pharmaceutical interventions that have been used to slow down these three pandemics and zoom in on the technological advances that were made in the progress. Finally, we discuss the evolution of epidemiological modelling, that has become an essential tool to support public health policy making and discuss it in the context of these three pandemics. While these pandemics are caused by distinct viruses, that ignited in different time periods and in different regions of the globe, our work shows that many of the determinants of their emergence and countermeasures used to halt transmission were common. Therefore, it is important to further improve and optimize such approaches and adapt it to future threatening emerging infectious diseases. This study was financed by FCT through the following projects: GHTM-UID/04413/2020, INTEGRIV (PTDC/ SAU-INF/31990/2017) and the scholarship PD/BD/135714/ 2018 and MARVEL (PTDC/SAU-PUB/4018/2021). This project has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No. 101046016. This work also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (PL and AT: grant number 101003688 – EpiPose project). PL gratefully acknowledges support from the Fonds voor Wetenschappelijk Onderzoek (FWO) via postdoctoral fellowship 1242021N and research project G0H0420N, and the Research council of the Vrije Universiteit Brussel (OZRVUB) via grant number OZR3863BOF. This research acknowledges funding from the Flemish Government through the AI Research Program.