Drivers of Infectious Disease Seasonality: Potential Implications for COVID-19

Abstract

Not 1 year has passed since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). Since its emergence, great uncertainty has surrounded the potential for COVID-19 to establish as a seasonally recurrent disease. Many infectious diseases, including endemic human coronaviruses, vary across the year. They show a wide range of seasonal waveforms, timing (phase), and amplitudes, which differ depending on the geographical region. Drivers of such patterns are predominantly studied from an epidemiological perspective with a focus on weather and behavior, but complementary insights emerge from physiological studies of seasonality in animals, including humans. Thus, we take a multidisciplinary approach to integrate knowledge from usually distinct fields. First, we review epidemiological evidence of environmental and behavioral drivers of infectious disease seasonality. Subsequently, we take a chronobiological perspective and discuss within-host changes that may affect susceptibility, morbidity, and mortality from infectious diseases. Based on photoperiodic, circannual, and comparative human data, we not only identify promising future avenues but also highlight the need for further studies in animal models. Our preliminary assessment is that host immune seasonality warrants evaluation alongside weather and human behavior as factors that may contribute to COVID-19 seasonality, and that the relative importance of these drivers requires further investigation. A major challenge to predicting seasonality of infectious diseases are rapid, human-induced changes in the hitherto predictable seasonality of our planet, whose influence we review in a final outlook section. We conclude that a proactive multidisciplinary approach is warranted to predict, mitigate, and prevent seasonal infectious diseases in our complex, changing human-earth system.

Keywords: Anthropocene; circannual; global change; infectious diseases; photoperiod; seasonality.

Figure 1.

Candidate drivers of disease seasonality. Circles show organisms implicated in disease transmission, black arrows indicate their interactions, and green arrows indicate the influence of environmental factors. Color version is available online.

Figure 2.

Seasonality of endemic human coronaviruses (CoV-OC43, CoV-NL63, and CoV-229E species) detected in a large urban patient population. The percentage (%) of human coronaviruses detected during routine real-time multiplex RT-PCR diagnostic testing of 84,957 episodes of respiratory illness in NHSGGC (primary and secondary care services), Scotland, UK, by calendar month categories from 2005 to 2017. Winter = December-February; Spring = March-May; Summer = June-August; Autumn = September-November. Note: Years 2009 and 2010 must be viewed with caution as only influenza-negative patients at risk of severe illness were tested for human coronaviruses in NHSGGC during the three waves of the influenza A/H1N1 pandemic in the United Kingdom; see also Nickbakhsh et al. (2020). Abbreviations: CoV = coronaviruses; NHSGGC = NHS Greater Glasgow and Clyde; RT-PCR = reverse transcription polymerase chain reaction.

Figure 3.

Annual and circannual cycle in an immune parameter. The capacity of whole blood to kill cultures of Staphylococcus aureus is shown for 3 experimental groups of songbirds, stonechats (genus Saxicola). Siberian (blue) and Kenyan (green) stonechats were kept in a common garden setup of annually changing European photoperiod (PP) over 1 year, where they showed distinct, population-specific annual cycles (Versteegh et al., 2014). Groups were measured per life cycle stage because the populations differed in duration of phases such as migration or molt (annual cycle stages 1-5: spring migration; breeding season; molt; autumn migration; winter). An additional Kenyan (red) group (Versteegh, Tieleman & Helm, unpubl.) that was kept under constant photoperiod showed similar, circannual cycles; curves indicate loess smoothing. Color version is available online.

Figure 4.

Seasonal gene expression changes in humans. (a) Peripheral white blood cells (PBMCs, from children) and subcutaneous adipose tissue (adults). (b) A group of 68 coregulated messenger RNAs (mRNAs) with winter tropism in the human immune system, enriched for genes associated with B-lymphocyte activation (KEGG). A cosinor model was used to analyze seasonality in mRNA expression (Dopico et al., 2015). Abbreviation: PBMC = peripheral blood mononuclear cell.

Figure 5.

Photoperiodic regulation of Siberian hamster blood leukocytes. Siberian hamsters housed in summer-like long-day (LD) photoperiods have lower levels of circulating leukocytes compared to winter-like short-day (SD) housed animals. (a) Unpublished RNA sequencing of blood leukocytes revealed several transcripts that are differentially expressed between LD and SD conditions. (b) Adult male hamsters were kept in LD (15L:9D) or SD (9D:15L) for 12 weeks. At the termination of the study, leukocytes were obtained from a retroorbital sample and cells were separated as described previously (Stevenson et al., 2014). Illumina sequencing and statistical analyses were conducted using the same procedures described in Bao et al. (2019). In LD, hamster leukocytes express the anthrax receptor 1 transcript, whereas there is a complete absence of its expression in SD. (c) Asterisks denote p < 0.001. Abbreviations: Pdgfb = Platelet-Derived Growth Factor Subunit B; Vill = Villin-like; Antxr1 = Anthrax receptor 1; Chpt1 = Choline Phosphotransferase 1; Snord15a = Small Nucleolar RNA, C/D Box 15A; Hsp40 = DnaJ Heat Shock Protein Family; Dnajb13 = Member B13; Rab27b = RAB27B Member RAS Oncogene Family; Mfsd4 = Major facilitator superfamily domain–containing protein 4; Cobl = Cordon-Bleu WH2 Repeat Protein; Akap12 = A-Kinase Anchoring Protein 12; Prr7 = Proline Rich 7 Synaptic; Dppa4 = Developmental Pluripotency Associated 4; DL = daylength; FDR = false discovery rate. ***p < 0.005.

Figure 6.

Drivers of emerging infectious disease events during 1940-2003. Data from Jones et al. (2008).

Figure 7.

Effects of anthropogenic global changes on drivers of infectious disease seasonality. Global change of the environment (orange box and arrows), and cultural and socioeconomic changes (gray), can affect the seasonality of infectious diseases directly, but also indirectly through their effects on environmental conditions (green). Color version is available online.