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PEARLS
The calendar of epidemics: Seasonal cycles of
infectious diseases
Micaela Elvira MartinezID*
Climate & Health, Department of Environmental Health Sciences, Mailman School of Public Health, Columbia
University, New York, New York, United States of America
*mem2352@cumc.columbia.edu
Introduction
Seasonal cyclicity is a ubiquitous feature of acute infectious diseases [1] and may be a ubiqui-
tous feature of human infectious diseases in general, as illustrated in Tables 1–4. Each acute
infectious disease has its own seasonal window of occurrence, which, importantly, may vary
among geographic locations and differ from other diseases within the same location. Here we
explore the concept of an epidemic calendar, which is the idea that seasonality is a unifying fea-
ture of epidemic-prone diseases and, in the absence of control measures, the local calendar can
be marked by epidemics (Fig 1). A well-known example of a calendar marked by epidemics is
that of the Northern Hemisphere, where influenza outbreaks occur each winter [2,3] (hence
the colloquial reference to winter as "the flu season"). In contrast, chickenpox outbreaks peak
each spring [4,5], and polio transmission historically occurred each summer [6].
Seasonal variation in infectious disease transmission plays an important role in determining
when epidemics happen; however, it is not the sole determinant. For instance, some infectious
diseases with known seasonal transmission, such as pertussis and measles, can display multi-
annual outbreaks, meaning their epidemics occur in multi-year intervals, such as every two or
four years, rather than annually. This is because the timing of these epidemics is determined
by a combination of (i) seasonal transmission and (ii) different processes shaping the number
of susceptible individuals in the population, a sufficient number of which is a prerequisite for
an outbreak.
Within the fields of infectious disease ecology and epidemic modeling, seasonal variation in
transmission is known as seasonal forcing [78]. Over the past century, attention has been paid
to detailing the cyclicity and mechanisms of seasonal forcing for a few diseases of public inter-
est, such as measles, influenza, and cholera (e.g., see contemporary work by [3,79,80]).
Despite these notable examples, disease seasonality has yet to be systemically and/or rigorously
characterized for the majority of infections.
Here, I aim to motivate future studies of disease seasonality by drawing attention to the
importance of seasonality in public health, medicine, and biology. I will explore documented
seasonal cycles in human infections, including notifiable and neglected tropical diseases. I also
aim to present a holistic view of hypothesized drivers of seasonality for each disease, with the
caveat that, for the majority of infections, the current state of the science is insufficient to draw
conclusions about seasonal timing, seasonal magnitude, and geographic variation in incidence.
Although published data regarding disease seasonality are limited for individual diseases, col-
lectively the body of work on disease seasonality is vast and reveals that infections—which may
differ enormously in their pathology and/or ecology—coalesce via underlying seasonal drivers.
In order to explore documented seasonal cycles in human infections, the websites of the
United States Centers for Disease Control and Prevention (CDC), World Health Organization
PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1007327 November 8, 2018 1 / 15
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OPEN ACCESS
Citation: Martinez ME (2018) The calendar of
epidemics: Seasonal cycles of infectious diseases.
PLoS Pathog 14(11): e1007327. https://doi.org/
10.1371/journal.ppat.1007327
Editor: Kimberly A. Kline, Nanyang Technological
University, SINGAPORE
Published: November 8, 2018
Copyright: ©2018 Micaela Elvira Martinez. This is
an open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Funding: Research reported in this publication was
supported by the Office Of The Director, National
Institutes Of Health of the National Institutes of
Health under Award Number DP5OD023100. The
content is solely the responsibility of the authors
and does not necessarily represent the official
views of the National Institutes of Health. The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
(WHO), and the European Centre for Disease Prevention and Control were searched to com-
pile a list of 60+ communicable diseases of public health interest. Careful attention was paid to
include neglected tropical diseases that may be underrepresented in disease notification sys-
tems. For each infection, online WHO disease information pages were used to determine
whether the disease is acute or chronic. When the nature of the infection could not be estab-
lished from the WHO disease information page, this information was gathered from online
CDC disease factsheets. Google scholar was then used to systematically search for information
regarding disease seasonality. For each of the diseases, a search was conducted using "[disease
name] AND season." When needed, I also added "AND human" to the search term for zoo-
notic diseases. When searches needed to be more specific, a search for "[disease name] AND
seasonality" was conducted. Most diseases had very few papers that specifically focused on sea-
sonality (based on their titles and abstracts); the most relevant paper(s) presented in top search
results were used in Tables 1–4. The method employed here was meant to provide a broad
overview of many infections as opposed to detailed information regarding any individual
infection. A short description of the seasonality and hypothesized seasonal drivers were then
summarized in Tables 1–4.
In the broadest sense, seasonal drivers can be separated into four categories: (1) environ-
mental factors, (2) host behavior, (3) host phenology, and (4) exogenous biotic factors. These
seasonal drivers may enter into disease transmission dynamics by way of hosts, reservoirs,
and/or vectors. In surveying the literature to gauge the breadth of seasonal drivers acting upon
human infectious disease systems (Tables 1–4), specific seasonal drivers were found to include
(a) vector seasonality, (b) seasonality in nonhuman animal host (i.e., livestock, other domestic
animals, or wildlife), (c) seasonal climate (e.g., temperature, precipitation, etc.), (d) seasonal
nonclimatic abiotic environment (e.g., water salinity), (e) seasonal co-infection, (f) seasonal
exposure and/or behavior and/or contact rate, (g) seasonal biotic environment (e.g., algal den-
sity in waterbodies), and (h) seasonal flare-ups/symptoms and/or remission/latency.
Environmental factors
Environmental factors, specifically climate conditions, are the seasonal drivers that have
received the most attention. This may be because they often covary with seasonal disease inci-
dence. Environmental drivers are abiotic conditions that influence transmission via their
effects on hosts and/or parasites; classic examples are temperature and rainfall, which influence
a variety of infectious diseases [81], but other examples include seasonal nonclimatic abiotic
environmental conditions, such as water salinity, which may impact water-borne pathogens.
Environmental factors can impact pathogen survival during transitions between hosts. Transi-
tions can take place during short time windows (e.g., for droplet-transmitted infections) or
long time windows (e.g., for parasites with environmental life stages). In addition to their
impact on pathogens, environmental drivers can also influence host susceptibility to infection
or vector population dynamics.
As for host susceptibility, environmental conditions can impact the host immune response
and increase cells’ susceptibility to infection [82] or pose seasonal challenges (such as food lim-
itations) that leave hosts vulnerable to infection or pathology [83], which has been proposed to
influence disease progression in individuals infected with HIV [35]. For directly transmitted
infections, environmental conditions can be major drivers of cycles in incidence, with influ-
enza and cholera transmission being notable examples (e.g., see [3,80]). The effects of climate
on flu transmission have been studied using population-level data coupled with transmission
models, as well as empirical animal studies [84], to demonstrate the effects of temperature and
humidity on transmission. Although climate conditions undoubtedly play a direct role in
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several directly transmitted infections, they may play a more nuanced role in vector-borne dis-
ease systems in which they modulate vector population dynamics and subsequently disease
transmission. For example, in the case of African sleeping sickness (Table 1), the rainy season
is hypothesized to modify tsetse fly distribution, which results in changes in human–tsetse fly
contact and subsequently African sleeping sickness incidence; in this case, we can classify the
seasonal driver as (1) vector seasonality alone or as (2) seasonal climate influencing vector sea-
sonality and vector seasonality having a downstream effect on seasonal exposure. Abiotic and
biotic seasonal drivers are therefore interconnected and not mutually exclusive.
Host behavior
Transmission seasonality is sometimes due to seasonal host behavior, specifically fluctuations
in transmission-relevant host contact rates throughout the year. Seasonal host behavior not
only includes seasonal behavior and/or exposure and/or contact rates in humans but also sea-
sonality in nonhuman animal hosts (i.e., livestock, other domestic animals, or wildlife). The
Table 1. Seasonal drivers of human infectious diseases. Drivers categorized as being related to (a) vector seasonality, (b) seasonality in nonhuman animal host (i.e., live-
stock, other domestic animals, or wildlife), (c) seasonal climate (e.g., temperature, precipitation, etc.), (d) seasonal nonclimatic abiotic environment (e.g., water salinity),
(e) seasonal co-infection, (f) seasonal exposure and/or behavior and/or contact rate, (g) seasonal biotic environment (e.g., algal density in waterbodies).
Infection/disease Type Seasonal
driver(s)
Description
African sleeping sickness Chronic a Tsetse fly distribution changes seasonally; expanded range during rainy season [7]
Anthrax Acute b Zoonotic disease with seasonality reported in wildlife and livestock; seasonality varies among location
and species [8]
Avian influenza Acute b Winter in both humans and poultry (in Asia) [9,10]
Bacterial Pneumonia Acute c, d, and e Peaks in midwinter (in the US); it is associated with influenza [11]
Brucellosis Acute b Spring and summer in wildlife and livestock; the timing relates to the birthing season; peaks in the
summer in humans [12]
Buruli ulcer Chronic c Varies by location; some studies have not observed seasonality [13]
Chagas disease Acute and
chronic
a Peaks in spring and summer in countries with distinct seasons [14]
Chickenpox acute f Peak in spring in the Northern and Southern Hemisphere [15]
Chikungunya Acute a Rainy season when vector density peaks. [16]
Cholera Acute c, d, and g Seasonality is stronger in countries further from the equator; outbreaks generally occur in warm months
[17]
Crimean-Congo
hemorrhagic fever
Acute a Seropositivity in livestock correlates with seasonal changes in tick parasitism; human cases correlate
with livestock seropositivity [18]
Cryptosporidium Acute c Increased risk of cryptosporidium associated with high ambient temperature and high rainfall [19]
Cutaneous leishmaniasis Acute and
chronic
a and b Strong seasonal variation with elevated incidence from October to March (in Tunisia). Seasonality may
be due to climate effects on the vector: blood-feeding sand flies [20]
Dengue fever Acute a Rainy season (in Thailand) [21]
Diphtheria Acute f Spring and summer (in Portugal) [22]
Dracunculiasis Chronic c, d, f, and g Dry season (in Nigeria) [23]
Ebola Acute b In wildlife the peak is in the dry season (in Gabon) [24]
Echinococcosis Chronic b Exposure to livestock carrying the infection is seasonal [25]
Escherichia coli
(pathogenic)
Acute b Seasonal in cattle; cattle are a source for human infection [26]
Foodborne trematodiases Chronic f Exposure is seasonal due to seasonal ingestion of infected snails [27]
Genital herpes Chronic f Elevated incidence in spring/summer and lower in winter (in the US) [28]
Gonorrhea Chronic f Peak cases in the summer and autumn (in the US) [28]
Since seasonal timing may differ among geographic areas, study location is indicated in parentheses.
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most well-known example of seasonal contact rates in humans is the aggregation of children
in schools during school terms that results in elevated transmission of measles (e.g., [1]).
Recent studies of seasonal contacts include the use of innovative data sources such as light-at-
night satellite imagery and mobile phone data to infer human mobility patterns throughout
the year [48,59]. Data required to quantify seasonal variation in human contact rates are
becoming increasingly available. For zoonotic diseases, however, it is contact with wildlife or
livestock that can drive seasonal transmission, such as with anthrax, Crimean-Congo hemor-
rhagic fever, Ebola, echinococcosis, and others (Tables 1–4). Characterizing the seasonal inter-
face among humans and wildlife/livestock poses unique challenges, especially when contacts
are occurring in remote areas, which is likely the case with Ebola and echinococcosis (Table 1).
From an ecological perspective, wildlife systems offer a rich arena to study the diverse ways
in which behavior impacts disease transmission. Understanding transmission within wildlife
is not only relevant for zoonotic infections but is also important for conservation ecology (i.e.,
in order to protect populations from disease-induced declines, such as is currently experienced
by Tasmanian devils, North American bats, and amphibians worldwide). More broadly,
understanding the ecology of disease transmission in wildlife can lead to insights that may be
Table 2. Seasonal drivers of human infectious diseases (continued from Table 1). Drivers categorized as being related to (a) vector seasonality, (b) seasonality in non-
human animal host (i.e., livestock, other domestic animals, or wildlife), (c) seasonal climate (e.g., temperature, precipitation, etc.), (f) seasonal exposure and/or behavior
and/or contact rate, (g) seasonal biotic environment (e.g., algal density in waterbodies), (h) seasonal flare-up/symptoms and/or remission/latency, (i) observed seasonal
incidence with no hypotheses regarding drivers.
Infection/disease Type Seasonal driver
(s)
Description
Haemophilus
influenzae
Acute i Slightly elevated incidence in winter (in the US) [29]
Hepatitis A Acute f and i Dry season (in Brazil) [30,31]
Hepatitis B Chronic h Seasonality is observed with elevated levels in spring and summer and/or autumn in some parts of the world,
whereas there is lack of seasonality in other parts of the world [31,32]
Hepatitis C Acute and
chronic
f Seasonality observed in some countries and absent in others; spring and/or summer peaks in Egypt, China,
and Mexico while there is a winter peak in India [31]
Hepatitis E Acute c Waterborne outbreaks occur during the rainy season or following flooding (in China) [33]
Herpes zoster
(shingles)
Acute and
chronic
i and h�Highest in August and lowest in winter (in Japan) [34]
HIV Chronic g There is some evidence to suggest there is seasonal variation in the progression to AIDS; hypothesized to be
related to seasonal nutritional deficiencies (study done in Uganda) [35]
Influenza Acute c Winter (in the Northern Hemisphere) [36]
Japanese
encephalitis
Acute a It is seasonal in the northern part of the tropical zone; outbreaks happen at the end of the rainy season, but
there is no seasonal pattern in tropical regions [37]
Lassa fever Acute c Increase in the number of Lassa fever cases during the dry season (in Nigeria) [38]
Legionellosis Acute c Peaks during hot months and particularly during humid periods (in the US) [39]
Leishmania Chronic a Transmitted by sand flies; domestic dogs are the main reservoir, and they are exposed during a discrete
transmission season [40]
Leprosy Chronic b Armadillos are the reservoir, and antibody prevalence is seasonal within them [41]
Leptospirosis Acute c Peaks when there is hot weather; usually in a rainy period (on all continents) [42]
Lyme disease Acute and
Chronic
a Peaks in summer around the time of maximal activity of the nymphal stage of the tick vector (in the US) [43]
Lymphatic filariasis Chronic a and c Transmission is intensified during the rainy season [44]
Malaria Acute a There is a spectrum of seasonal strength; some regions have strong seasonality and no seasonality in others
[45]
Since seasonal timing may differ among geographic areas, study location is indicated in parentheses.
�Indicated by author.
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applied to human health. Wildlife and urban human populations exist at opposite extremes of
a continuum of exposure and/or subjection to natural environmental cycles. Due to the poten-
tially more extreme effect of natural environmental cycles on wildlife disease systems—includ-
ing climatic influences on wildlife behavior—wildlife could serve as a model for developing
methods and conceptual frameworks needed to disentangle the contribution of environmental
cycles and behavior from other drivers of disease transmission.
For example, wildlife may be a useful study system for sexually transmitted infections
(STIs). Unlike humans, who might have moderate fluctuations in sexual contacts throughout
Table 3. Seasonality of human infectious diseases (continued from Tables 1and 2). Drivers categorized as being related to (a) vector seasonality, (b) seasonality in non-
human animal host (i.e., livestock, other domestic animals, or wildlife), (c) seasonal climate (e.g., temperature, precipitation, etc.), (f) seasonal exposure and/or behavior
and/or contact rate, (g) seasonal biotic environment (e.g., algal density in waterbodies), (h) seasonal flare-up/symptoms and/or remission/latency, (i) observed seasonal
incidence with no hypotheses regarding drivers.
Infection/disease Type Seasonal
driver(s)
Description
Marburg Acute b Seasonal incidence in bat reservoirs (in Uganda); seasonal peaks coincided with the twice-annual
birthing season [46]
Measles Acute f Elevated transmission driven by aggregation of children in school; seasonality in developing countries
related to agricultural cycles [47,48]
Meningococcal disease Acute c and h Incidence varies seasonally in both tropical and temperate countries. Elevated incidence during the dry
season (in sub-Saharan Africa). [49]
MERS-CoV Acute b Introductions into humans are seasonal and are more frequent during the camel calving season. [50]
Onchocerciasis (river
blindness)
Acute and
chronic
a Higher transmission potential in the rainy season when vector abundance and infection is elevated (in
Nigeria) [51]
Pertussis Acute i and f�Higher incidence June through October (in the US) [52]
Plague Acute a, b, c, f, and g The seasonality varies among countries and is dependent on seasonality of reservoir and vector species
and in some cases agricultural cycles [53]
Poliomyelitis Acute i, c�, and h�Epidemics occurred during the summer (in the US) [6]
Rabies Acute b Rabies is seasonal in bats, which are a source of human exposure [54]
RSV Acute i and c�Peaks in winter months in temperate regions; less pronounced seasonality in the tropics [55]
Rift Valley fever Acute a and c Associated with periods of heavy rainfall [56]
Rotavirus Acute i and c�Geographical gradient in seasonality; peaks in December/January in the Southwest US and April/May
in the Northeast US [57]
Rubella Acute f Two seasonal peaks in transmission per year in Kenya; late-winter to early-summer peaks in the US [58,
59]
Salmonellosis Acute i Increased number of isolates in the warm spring months (in Tunisia) [60]
Schistosomiasis Chronic b and c Transmission is seasonal; two seasonal peaks per year (in Tanzania) [61]
Scrub typhus Acute a, c, and f Seasonality depends on activity of vectors (i.e., chiggers) and humans. Seasonality varies geographically.
Some areas (in Japan) have strong seasonal transmission, and others have relatively stable transmission
[62]
Shigella Acute c Elevated incidence in summer (in Massachusetts, US) [63]
Smallpox Acute c Associated with dry weather [64]
Soil-transmitted helminth
infections
Chronic c and g Hookworms undergo seasonal arrested development, which affects the acquisition of infection in
humans; there is also seasonal acquisition of roundworm infections [65,66]
Syphilis Chronic f Higher incidence in summer (in China) [67]
Taeniasis (cysticercosis) Chronic b and f Seropositivity varies seasonally in livestock, which are the source of human infection (in Romania) [68]
Tetanus Acute c and f Peak in midsummer (in the US) [69]
Trachoma Acute and
chronic
a More common in the wet season when the fly vector is most abundant (in Australia) [70]
Since seasonal timing may differ among geographic areas, study location is indicated in parentheses.
�Indicated by author.
Abbreviations: MERS-CoV, Middle East respiratory syndrome coronavirus; RSV, Respiratory Syncytial Virus.
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the year [85], in some mammal species, there is a complete absence of sexual contact (and thus
transmission) outside of the breeding season. I propose that isolation in time, such as this, is a
much more extreme form of seasonal forcing than seen in human infectious disease systems.
Isolation in time could also occur for parasites with other transmission modes, in addition to
STIs. Although it is an unexplored area of research, the study of isolation in time may reveal
pathogen metapopulation structure and parasite adaptations for surviving through transmis-
sion troughs. Discrete windows of transmission are likely to have evolutionary consequences
for parasite life history. Cattadori and colleagues [86] pointed out that, when transmission is
restricted to a short seasonal window, natural selection will favor parasites with "long-lived
infective stages." I further speculate that transmission isolated in time will have dynamical con-
sequences that make these disease systems unique from those with more continuous transmis-
sion cycles.
Sexual contact is not the only transmission-risk–elevating behavior that can display season-
ality. Seasonal engagement in risk-taking behavior may occur in other disease contexts, includ-
ing for infections transmitted during bouts of fighting. For example, the transmission of facial
tumor disease among Tasmanian devils—which is caused by an infectious cancer—is facili-
tated by aggressive behavior. During a fight, the cancer cells from a facial tumor of an infected
devil can be transferred into the wounds and mouth of a susceptible devil, resulting in infec-
tion. The Tasmanian devil contact network varies between the mating and nonmating seasons,
and this could influence the transmission of this infectious cancer [87,88]. Although mating
and aggression can elevate disease risk, it is important to acknowledge that some behaviors can
also mitigate disease risk. In wildlife, disease mitigation behaviors include grooming to remove
ectoparasites (as observed in birds and primates) and self-medication (as observed in primates,
birds, and monarch butterflies) [89,90]. There are, however, very few studies of seasonality in
risk-taking and risk-mitigation behavior. This is yet another area in which wildlife could serve
as a useful model system.
As previously noted, mobility patterns are an aspect of host behavior that can also season-
ally structure disease risk via the geographic localization of hosts. In humans, the most notable
example is the movement of people in and out of cities. In Niger, for instance, the resulting
changes in population density from migration is believed to be the primary driver of measles
Table 4. Seasonality of human infectious diseases (continued from Tables 1–3). Drivers categorized as being related to (a) vector seasonality, (c) seasonal climate (e.g.,
temperature, precipitation, etc.), (h) seasonal flare-up/symptoms and/or remission/latency,(i) observed seasonal incidence with no hypotheses regarding drivers.
Infection/
disease
Type Seasonal driver
(s)
Description
TB Chronic c and h Approximately 24% more TB notifications in the summer verses the winter (in the UK) [71]
Typhoid fever Acute i and c�Peaks around July (in China) [72]
Viral
meningitis
Acute i Higher in the summer, when enterovirus transmission peaks (in Israel) [73]
West Nile virus Acute a and c Peaks July through August in the temperate zones of the Northern Hemisphere [74]
Yaws Chronic h More cases in the wet season; hypothesized to be due to more clinical relapse during the wet season; transmission may be
relatively constant throughout the year [75]
Yellow fever Acute a and c Seasonal changes in the distribution and density of the vector Aedes aegypti; transmission peak was historically in
autumn (in the Americas) [76]
Zika Acute a and c Seasonal changes in incidence are expected to be driven by seasonal fluctuations in the vector population (the A.aegypti
mosquito) [77]
Since seasonal timing may differ among geographic areas, study location is indicated in parentheses.
�Indicated by author.
Abbreviation: TB, tuberculosis.
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transmission seasonality [48]. Similarly, in wildlife, hosts seasonally engage with different
aspects of their environment. For hosts that migrate or hibernate, contact with risky environ-
ments can be seasonal [91]. Migration and hibernation are part of host phenology, which has
been implicated in several infectious disease systems [91–94].
Phenology
Host phenology includes host life history, annual cycles (e.g., migration and hibernation), and
endogenous circannual rhythms (i.e., endogenously driven seasonal changes in physiology)
[94]. Relevant host phenology includes, but is not limited to, seasonal changes in reproduction,
seasonal restructuring of immunity, cycles of metabolism and body condition, hibernation,
and migration. Phenology is not only a feature of hosts but also of reservoirs, vectors, and
some parasites themselves (particularly helminths). Unlike environmental drivers and host
behavior, which can affect diseases dynamics by (i) seasonally forcing transmission in hosts,
reservoirs, and vectors, phenology can drive seasonality via additional mechanisms of action,
which include the modulation of (ii) susceptible recruitment (via reproduction), (iii) suscepti-
bility to infection, (iv) infectiousness, (v) the recovery rate, (vi) the mortality rate (both natural
and disease-induced), and (vii) symptomatology and/or pathology.
Each of the seven mechanisms of action could leave a unique imprint in long-term inci-
dence data, as proposed in [94]. These mechanisms and their drivers, therefore, would have
different consequences for disease dynamics. Using models, such as that schematized in Fig 1,
statistical inference and simulation studies could be conducted to identify the dynamical
effects of various seasonal mechanisms acting in isolation and/or in combination. Simulation
studies could provide a foundation for determining the types of data required for distinguish-
ing among seasonal mechanisms and/or drivers and how factors such as resonance can influ-
ence the ability to detect seasonal drivers. For example, Martinez-Bakker and colleagues [94]
used a simulation study to demonstrate the demographic and transmission regimes under
which human birth seasonality is expected to have a meaningful impact on measles incidence
in the face of strong seasonal forcing from transmission during school terms.
This challenge of identifying seasonal drivers and their mechanisms of action becomes
greater when considering chronic infections, vector-borne diseases, and parasites with com-
plex life histories and phenology of their own. In reviewing seasonal drivers of human disease
systems for Tables 1–4, human phenology seemed to be particularly relevant for diseases that
have seasonal flare-ups/symptoms and/or remission/latency; this includes some chronic infec-
tious diseases, such as tuberculosis and yaws, along with Meningococcal disease, which is
acute (Tables 2–4). Although the study of human phenology is a relatively new research area,
phenology of vectors and nonhuman animals is well studied and could be the most common
cause of vector seasonality and seasonality in nonhuman animal hosts, which impact diseases
such as Zika (Table 4) and Middle East respiratory syndrome coronavirus (MERS-CoV)
(Table 3).
Exogenous biotic factors
In addition to abiotic, behavioral, and phenological features of host–parasite systems, hosts
and their parasites are embedded within ecological communities that have additional seasonal
aspects. We can refer to biotic factors driven by ecological communities as exogenous biotic
factors because they are exogenous to any given host–parasite dyad, host–vector–parasite
triad, or multi-host system. Exogenous biotic factors include (1) interactions that take place
within hosts—specifically parasite–parasite interactions—and (2) interactions within the eco-
logical community of hosts, reservoirs, and vectors.
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During some co-infections, parasite–parasite interactions can occur directly or be mediated
via the host immune system, when parasite species impact each other’s population dynamics
indirectly via their effect on the host immune system. Parasite–parasite interactions can result
in parasite fitness being elevated (i.e., facilitation) or dampened (e.g., competition) [95–97].
The seasonality of individual infectious diseases likely imposes seasonal structure on co-infec-
tions and thus the presence of parasite–parasite interactions, as has been implicated in the sea-
sonality of bacterial pneumonia (Table 1).
Fig 1. Epidemic calendar. The concept of an epidemic calendar is illustrated in the top panel. Infectious diseases are
seasonal, especially the occurrence of acute and epidemic-prone diseases. In any given population, infectious diseases
are distributed throughout the year. Annual cycles of infectious disease are a ubiquitous feature of infection (Tables 1–
4). The illustration depicts the wintertime seasonality of flu, springtime peaks of varicella (i.e., chickenpox), and the
summertime occurrence of gonorrhea and polio, in the Northern Hemisphere. The bottom panel is a SIR schematic for
the transmission of human infectious diseases, which include acute and chronic diseases, those that are vector-borne,
and those that are zoonotic with animal reservoirs. The vector, human, and reservoir hosts populations are partitioned
into individuals who are susceptible to infection, infected, and recovered and immune. Seasonality may enter into any of
the eight key elements of the system: (1) susceptible recruitment via reproduction, (2) transmission, (3) acquired
immunity and recovery, (4) waning immunity, (5) natural mortality, (6) symptomatology and pathology (which may be
acute or chronic, depending on the disease), (7) disease-induced mortality, and (8) cross-species transmission. Disease
illustrations reproduced from Google Medical Information. I, infected; R, recovered and immune; S, susceptible.
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Outside of the host, community ecology becomes particularly relevant for disease transmis-
sion in multi-host systems, such as Lyme disease [95,96,98]. Community ecology is particu-
larly important when there is heterogeneity in host and/or vector competence in a multi-
species disease system. This is because the abundance of competent hosts and/or vectors can
determine the transmissibility and maintenance of infection [98]. Each host and/or vector in a
vector-borne or multi-host disease system has its own set of ecological interactions (e.g., com-
petitive, commensal, parasitic, etc.) that can indirectly affect the disease of interest. The phe-
nology of hosts, reservoirs, and vectors will give rise to seasonal changes in ecological
community composition. Taken together, parasite–parasite interactions within hosts and eco-
logical interactions outside hosts undoubtedly display seasonality, as do nearly all aspects of
ecology, and this can influence the dynamics of diseases of public interest.
Understanding seasonality
Seasonality is an inherent feature of ecological systems, and seasonal incidence is a feature of
both acute and chronic infectious diseases (Tables 1–4). It is, therefore, important to conceptu-
alize the epidemic calendar (Fig 1) from the lens of "everything is seasonal." The utility of this
lens is that it forces us to carefully consider mechanisms behind disease seasonality, thus pre-
venting what could, in some cases, be the misleading establishment of correlative relationships
between seasonal phenomena and infectious disease incidence. Focused attention on building
theory that will provide a deeper understanding of seasonal mechanisms and learning how to
identify imprints of seasonal drivers in disease data could bring rapid advancement to the field
of disease seasonality. In general, if "everything is seasonal," then everything will covary (usu-
ally with some phase shifts). Therefore, seasonal covariance alone is not useful for establishing
seasonal drivers. Instead, long-term parallel data of potential seasonal drivers and disease inci-
dence should be confronted with mechanistic transmission models. There is evidence to sug-
gest that the information contained in interannual variation and anomalous years holds a key
to establishing causal inference of seasonal forcing [99].
A thought experiment can be used to illustrate how anomalous years and interannual varia-
tion could be used to establish causal mechanisms of disease seasonality. Let’s consider a
human disease with peak incidence in the summer, such as polio [6]. Because incidence peaks
in summer, it would have a strong positive relationship with temperature, photoperiod (day
length), and many other summer-related features of the environment and human populations.
To highlight how noncausal seasonal factors could misleadingly correlate with disease inci-
dence, we could quantify what would be a likely strong positive relationship between disease
incidence and the sale of summer-related items that have nothing to do with transmission
(e.g., bathing suit or ice cream sales). Let’s imagine we build a transmission model for this
infection and test three potential seasonal drivers (e.g., temperature, photoperiod, and bathing
suit sales). We find that all three model variants capture the seasonal structure of the epidemics
because they all contain a covariate with the necessary seasonal structure. However, if the dis-
ease displays (1) interannual variation in epidemic size and/or (2) anomalous years with differ-
ences in epidemic timing that cannot be explained by demography or susceptible recruitment
dynamics, then only the model with data from the causal driver would improve our ability to
predict the variation in incidence observed among years.
By confronting disease incidence data with transmission models and testing the relevance
of various demographic, ecological, behavioral, and physiological covariates, we could identify
potential seasonal drivers on the population level. For some seasonal drivers, such as seasonal
changes in host immunity, we would, however, still be tasked with understanding the mecha-
nism of action within the host. The effects of seasonal drivers are multi-layered. To better
PLOS Pathogens | https://doi.org/10.1371/journal.ppat.1007327 November 8, 2018 9 / 15
understand seasonality, we must work at multiple organizational levels of science. Geophysical
factors, host population ecology, and within-host biology will need to be integrated in the prac-
tice of studying seasonality. As previously mentioned, many infectious diseases, which might
differ greatly in multiple aspects of their biology, can share the same seasonal driver(s). An
immediate way to advance the field of disease seasonality is to leverage the rich weekly and/or
monthly datasets available for notifiable diseases and combine them with models and data on
potential drivers. By coupling models and data, hypothesis testing can be done to assess sea-
sonal drivers and their modes of action. These data and models can be applied to multiple dis-
ease systems in parallel. For instance, parallel study of the seasonal drivers of (a) flu,
respiratory syncytial virus (RSV), bacterial pneumonia, and pertussis, or (b) polio, typhoid,
and rotavirus, or (c) Zika, dengue, chikungunya, and yellow fever would be a logical start.
Uncovering the mechanisms of seasonality for disease systems would empower the public
health community to better control infection. This sentiment was shared in 1949 by polio epi-
demiologist H. Gear, who wrote: "It must be admitted that the reasons for the seasonal inci-
dence of poliomyelitis remain obscure. When they have been elucidated perhaps much of the
epidemiology of this disease will be solved" [100].
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