The review was prepared by Polina Rogacheva, Alla Loseva
This spring, a pandemic made us wonder: will the new coronavirus disappear with the arrival of heat? After all, other infections that affect the respiratory tract, like flu and colds, are much less likely to occur in the warm period. This is partly because particles of respiratory viruses last longer in dry winter air without falling with drops of water – which means that people have more time to inhale them. Besides, dry and cold air damages the cells that line the respiratory tract, and warm, moist air, on the contrary, maintains a layer of mucus, which protects against harmful particles (Moriyama, Hugentobler, and Iwasaki 2020).
The new SARS-CoV-2 coronavirus has recently been found to mostly spread through airborne transmission (Zhang et al. 2020). However, the studies do not positively say that the epidemic will attenuate in the summer. The spread of the virus, apparently, is not sufficiently affected by either short-term weather changes or long-term climate changes, as evidenced by the spread of the pandemic even in warm and humid areas.
For viruses in general, the role of climate is not only that it affects the survival of infections outside the host organism, and not only in the seasonal weakening of immunity. There is also a delayed effect of climate on the spread of viruses. For example, due to global warming and human encroachment into nature, the Ebola virus can go beyond the current foci of infection and spread across Africa, including large transport hubs. The global warming factor in this example is not the main one, but climatic conditions affect the spread of infections through mechanisms of different levels.
Let’s see what other topics in connection with the weather and climate changes are raised by epidemic researchers. For the review, we have performed a systematic search in the scientific literature database Scopus and have built a map of publications based on their reference lists (Figure 1). Proximity in this map and belonging to the same cluster mean that the papers cite the same publications, therefore the papers are likely to consider similar issues. The map is built using VOSviewer software.
Publications are split into six clusters:
- navy, upper left: tick-borne viruses,
- purple, bottom left: malaria,
- blue, at the bottom: main reviews on seasonality,
- gray, bottom right: flu,
- light blue, center: climate change,
- yellow, top right: intestinal bacteria (not covered in the review).
Navy cluster: tick-borne viruses
The cluster is focused on viruses and infections that cause vector-borne diseases. These are diseases that are transmitted to people only from insects (mainly mosquitoes, ticks, and flies). Vector-borne diseases account for more than 17% of all infectious diseases and over 700,000 deaths annually.
Cluster publications discuss tick-borne viruses. In recent decades, the number of cases of tick-borne encephalitis among people has increased, and its geographical coverage has expanded to the Americas, Africa, and several regions of Europe. Climate is one of the factors that determine what species of ticks are found in a given geographical region (Estrada-Peña and de la Fuente 2014). Therefore, there are new risks for humans to encounter a vector-borne disease.
Climate affects the spread of such infections such as the Zika virus, Dengue virus, malaria, and Lyme disease (see Rogers and Randolph 2006 for a review). For example, when the temperature rises, ticks go down for water from the upper layers of the vegetation where they usually live and infect small rodents that carry the virus further. Moreover, if there is a drought, then ticks prefer to save moisture without moving and, accordingly, without transmitting the virus to other carriers (Randolph and Storey 1999). Here, the humidity factor is more important than temperature.
The cluster also included publications that mention the spread of viruses by bats and its seasonal patterns (Olival and Hayman 2014).
Purple cluster: malaria
Malaria is the most important and dangerous of the infections that are transmitted by parasites. It causes more than a million deaths per year (Greenwood et al. 2005). It was malaria that the first models of the spread of infections were dedicated to, the provisions of them are still used in epidemiology (Smith et al. 2012).
Environmental conditions strongly influence the transmission of malaria, and the models of its spread now take weather data into account (Hoshen and Morse 2004). The emergence and reintroduction of malaria are also more dependent on humidity than on temperature (Parham and Michael 2010) since malaria mosquitoes breed during the rainy season (Pascual et al. 2008). Nevertheless, infected people transmit the infection over long distances: where they come, mosquitoes become infected from them, even if weather conditions were not conducive (Wesolowski et al. 2012).
Blue cluster: main reviews on seasonality
In this cluster, the main publications on the influence of seasonality on the spread of viruses appear.
Altizer et al. (2006) consider the spread of infections to be affected by seasonal changes in how virus hosts behave, and the number of their contacts with susceptible populations; breeding periods of virus hosts; seasonal fluctuations in immunity.
Thus, flu and respiratory infections are common in winter, when children are constantly in contact at school, while the malaria example described above illustrates the factor of host multiplication. In the case of immunity, the production of antibodies depends on the production of melatonin, and it is lower when daylight is short (Dowell 2001). In winter, vitamin D production is also lower, which negatively affects immunity (Cannell et al. 2006).
Grassly and Fraser (2006) add to this classification the factor of virus survival outside the host organism. It depends on humidity, temperature, exposure to sunlight, acidity, and salinity.
For example, rotaviruses and noroviruses survive at low temperatures, so the peak incidence of gastroenteritis occurs in the winter months. The influenza virus lasts longer in the air during the cold period, when humidity is low, especially indoors, and aerosol particles with the virus do not fall in drops of water.
One illustrative and well-studied example of seasonal illness is measles. Its modeling has a long history, measles epidemics are simulated by stochastic models (Earn et al. 2000), which reflect frequent attenuation alternating with irregular large outbreaks (Ferrari et al. 2008). More general models also make it possible to assess how the number of people without immunity affects the consequences of the epidemic: either a new outbreak of the disease after some time, or a disease-free year – a “skip” (Stone, Olinky, and Huppert 2007).
Gray cluster: flu
This cluster unites empirical studies of influenza epidemics. As shown by Dushoff et al. (2004), even insignificant seasonal factors can explain the dynamics of influenza incidence.
One of the most famous types of flu is influenza A. The transmission of the influenza virus and its survival in the environment is influenced by the relative (Lowen et al. 2007) and absolute humidity (Shaman and Kohn 2009; Shaman et al. 2010). In regions with a temperate climate, absolute humidity has a pronounced seasonal cycle. The driest air is in winter, so in the Northern Hemisphere the flu season lasts from November to March, and in the Southern Hemisphere from May to September.
But seasonal epidemics are not always explained by humidity. Nelson and Holmes (2007) review evidence of other factors. For example, in waterfowl, influenza epidemics also occur in August-September, which is most likely due to the increasing density of flocks before migration, and the lack of immunity in fledglings. In the tropics, flu is present year-round despite a warm, humid climate, although the incidence peak sometimes occurs during the rainy season. Still, there are still little systematic data to study the flu in the tropics.
The authors also mention the factor of mobility (Balcan et al. 2009) and the fact that the spatial distribution of the virus corresponds to the working routes more than to mere geographical proximity of settlements (Viboud et al. 2006), although at the local level the flu is still mainly transmitted by pupils.
Light blue cluster: climate change
The most popular publication in this map also thematically belongs to this cluster, as it is related to the effects of regional climate change on human health (Patz et al. 2005). The authors note that many common diseases are associated with climate change, from cardiovascular diseases caused by heat waves to malnutrition due to crop failures, and infectious diseases.
Relating the occurrence or reappearance of the disease to climate change is problematic since there is almost no high-quality longitudinal data to separate the effect of climate change from the influence of other factors. However, the authors mention that global warming is already becoming the cause of greater morbidity and mortality in the foci of infections. Regions that are particularly vulnerable to the spread of infections due to climate change are temperate latitudes, where warming will be especially noticeable; regions along the shores of the Pacific and Indian Oceans that are affected by the El Niño climate anomaly; and sub-Saharan Africa, where the sprawl of cities and the urban heat islands can exacerbate the epidemiological situation.
Gubler et al. (2001) give an overview of the effects of climate change on diseases that are spread by insects and rodents. Researchers emphasize that infections are transmitted from tropical countries to temperate climates and survive in them.
Given this, other studies of the cluster on individual viruses are important. These are arboviruses that are distributed in the tropics and from arthropods through wildlife and livestock transmitted to humans (Weaver and Reisen 2010). It is also a Dengue fever virus (Lambrechts et al. 2011; Wearing and Rohani 2006), whose epidemic potential is increasing under global warming (Patz et al. 1998). Other tropical infections are being discussed: Japanese encephalitis (Misra and Kalita 2010), Zika virus (Barrera, Amador, and MacKay 2011), West Nile fever virus (Kilpatrick et al. 2006). All of them cause fever, headache, and other specific symptoms, as well as consequences of varying seriousness.
West Nile fever virus, for example, is more likely to be transmitted during the hot season (Hartley et al. 2012), partly because people wear more open clothing and prefer to spend time outside after sunset when mosquitoes are active. Especially often mosquitoes come in contact with those who do not have an air conditioner or reasons to stay at home in the evening – for example, there is no computer or TV (Reisen 2013). Thus, not only seasonal and climatic but also socio-economic factors play an important role in the spread of diseases.
Please proceed to page 2 to see general reviews on the climate influences on virus transmission and the description of our data.
General reviews
- Altizer, Sonia, Andrew Dobson, Parviez Hosseini, Peter Hudson, Mercedes Pascual, and Pejman Rohani. 2006. “Seasonality and the Dynamics of Infectious Diseases.” Ecology Letters 9(4):467–84.
- Grassly, Nicholas C., and Christophe Fraser. 2006. “Seasonal Infectious Disease Epidemiology.” Proceedings of the Royal Society B: Biological Sciences 273(1600):2541–50.
- Haines, A., R. S. Kovats, D. Campbell-Lendrum, and C. Corvalan. 2006. “Climate Change and Human Health: Impacts, Vulnerability, and Mitigation.” The Lancet 367(9528):2101–9.
- Lofgren, Eric, N. H. Fefferman, Y. N. Naumov, J. Gorski, and E. N. Naumova. 2007. “Influenza Seasonality: Underlying Causes and Modeling Theories.” Journal of Virology 81(11):5429–36.
Data source: Scopus bibliographic database. The search was made by titles, abstracts and keywords of publications using the term seasonal changes in virus spread. The search resulted in approximately 4000 publications, the 2000 most cited ones were selected for analysis.
Search query:
TITLE-ABS-KEY ( season* W/2 chang* OR variat* OR forcing AND *virus* OR *infect* OR strain AND transmi* OR spread )