2. CAPÍTULO II
3.2 Análisis de la Competencia
Hay et al. (2013) state that understanding the factors which are involved in the genesis of disease outbreaks is foundational to the development of an early warning disease system. Two sets of factors are mentioned which may attribute to the occurrence of yearly epidemic events, namely extrinsic factors (associated with climatic phenomenon) and intrinsic factors (associated with host and pathogen population dynamics) (Hay et al. 2013). Although climatic factors were specifically studied for instances of two vector-borne diseases, contextual factors play a role in disease dynamics regardless of the transmission mode, which is discussed within this section.
The first subsection (§2.4.1) relates to environmental factors which may affect disease dynamics, whereas §2.4.2 relates more to factors affecting the host population dynamics. These contextual factors are typically not incorporated explicitly in modelling approaches, possibly due to the complexity in realistically capturing these contextual characteristics within a model. The consideration of these factors are, however, useful to inform and highlight the various factors which affect disease dynamics.
It is worth noting that human activities which may play a role in disease transmission are omitted from detailed analysis, not limited to the following factors:
Mining activities and draining of wetlands; Agricultural activities and deforestation; Resistance to control and treatment methods; War; and
Urbanisation.
It is not denied that the above mentioned factors may affect disease dynamics on a high-level. For instance, the Second World War contributed to dengue fever emergence within South-East Asia through infantry movement (Ooi & Gubler 2008). Additionally, agricultural activities may alter environmental disease reservoirs and urbanisation in turn affects population density and proximity of individuals to each other (Reiter 2001). The challenge with incorporating human activities relates to the extreme difficulty in linking human factors realistically to disease dynamics and is therefore not included within the scope of this research.
2.4.1 Environmental factors
The transmission dynamics of diseases, especially that of vector-borne diseases, are greatly influenced by climatic factors (Reiter 2001). These factors include climate and seasonality, discussed in the remainder of the section.
Climate
Within the context of climate (e.g. temperature, rainfall, humidity), vector-borne diseases are the most frequently studied (Lipp et al. 2002). Furthermore, especially in tropical areas, climate may affect the transmission dynamics of vector-borne diseases. For example, Hay et al. (2013) mentions that mosquitoes responsible for transmitting malaria and dengue parasites are extremely sensitive to climate and variations thereof may affect vector populations. The effect of climatic factors may also vary according to the ecology of the vector, in addition by geographic region, which adds to the complexity of quantifying the influence of climatic conditions on disease transmission dynamics (Reiter 2001).
Theoretically, higher temperatures reduce the extrinsic incubation period of vectors, in addition to potentially increasing the frequency of biting and laying of eggs (Reiter 2001). This may potentially increase the transmission probability, as well as the survival rate of the vectors. Temperature may also affect human exposure to vectors, by affecting time spent in the outdoors and opening of windows in buildings to allow for ventilation (Xu et al. 2017).
Rainfall aids transmission of vector-transmitted disease by development of breeding sites and is one of the key short-term drivers affecting vector-borne disease dynamics (Reiter 2001; Xu et al. 2017). On the other hand, drought may remove sources of standing water, but may result in sources of flowing water to become stagnant and potentially result in the formation of new breeding sites (Reiter 2001).
The effect of climatic factors are not limited to vector-borne diseases. For instance, in the context of pertussis (i.e. respiratory disease), temperature is studied in relation to daily case numbers of pertussis notifications (Huang et al. 2017). Similarly, associations between climatic conditions and mumps prevalence are also studied (Li et al. 2016). In the context of rotavirus (i.e. a disease transmissible by means of body fluid and water contact), the association between climatic conditions and rotavirus prevalence is also determined (Van Gaalen et al. 2017).
Seasonality
Seasonality affects climate and in turn may drive disease dynamics. Reiter (2001) mentions that regions with a typical mild climate may experience summer temperatures which may be as high as the warm seasons of the tropics, whereas the tropical regions do not experience cold winters. This especially affects vector-borne diseases, as the vectors such as mosquitoes may be eliminated during winter, preventing vector-borne disease transmission.
Another example is that of cholera (a disease spread by means of water contact) dynamics in India, which illustrates a complex relationship between climate and seasonality. In a study performed in India, wetter regions typically experience 2 major annual peaks in cholera outbreaks, whereas drier regions typically only experience one major annual outbreak (Altizer et al. 2006). The onset of the
cholera outbreaks in the drier provinces are correlated to the onset of monsoons. However, in wetter provinces the outbreaks are more prevalent during the dry months and decline during monsoon rains (Altizer et al. 2006). This may be explained by the phenomenon that temperature affects cholera dynamics in wetter regions and rainfall affects cholera dynamics in drier regions (Altizer et al. 2006). The seasonality of monsoon rains and seasonal temperature variation on cholera dynamics illustrate the manner in which seasonality may affect disease dynamics.
Seasonal changes may also affect the behaviours of hosts, pathogens and vectors and in turn influence disease dynamics. These include changes in behaviour and contact rates of hosts and pulses of births which may be more vulnerable during winter periods and harsh weather, due to the effect on herd immunity and variations in immune defences (Altizer et al. 2006).
The effects of seasonality are not limited to vector-borne and water contact diseases. For instance, in the context of H1N1 (i.e. a respiratory disease), seasonality of disease instance are determined and analysed (Balcan et al. 2009). Similarly in the context of measles (i.e. a respiratory disease), the disease prevalence is linked to the seasonality of dust events (Ma et al. 2017).
2.4.2 Population demography and dynamics
In addition to environmental factors, the structure and composition of a population (i.e. population demography) play a role in disease dynamics. Factors which relate to demography, population density and spatial distribution, migration and socio-economic factors are discussed in the remainder of the section.
Demography
The demographics of a population play a role in the composition of a population and is typically included when conducting epidemiological research (Joubert 2014). These factors include the following, namely:
Age; Sex; and
Mortality and natality.
The selection and description of the above mentioned factors are used to model factors such as age- related disease susceptibility, contact patterns and changes in population size and distribution. For instance, in the context of pertussis, age and waning immunity are included in the modelling application to analyse the impact of an infant vaccination programme (Campbell et al. 2016). Similar studies are completed in the context of measles (i.e. respiratory disease) in relation to age-specific mixing, vaccination and outbreak risk (Bhattacharyya & Ferrari 2017). In the context of rotavirus (i.e. a disease transmissible by means of body fluid and water contact), the variability of population demographic composition, especially the variation in birth rate, is analysed and linked to the
occurrence of rotavirus epidemics (Pitzer et al. 2009). In the context of Ebola (i.e. a disease transmissible by means of direct contact, sexual contact, body fluid contact and respiratory contact), the age-related probability of contact is included in the modelling application to estimate disease transmission (Siettos et al. 2016a).
Population density and spatial distribution
Population density and spatial distribution affect the proximity of individuals to each other in a population, in turn affecting disease dynamics, regardless of the transmission mode. In the context of SARS (i.e. a respiratory disease), the connections between regions (i.e. spatial spread) and the population density are incorporated in the modelling application to model disease transmission (Yoneyama et al. 2010). In the context of smallpox, (i.e. a disease transmissible by means of direct contact, respiratory contact and body fluid contact) the population density is included in a simulation approach to test the efficacy of different vaccination strategies (Brouwers et al. 2010). In the context of Ebola (i.e. a disease transmissible by means of direct contact, sexual contact, body fluid contact and respiratory contact), the spatial spread of individuals are incorporated to analyse the dynamics of super spreading events (Lau et al. 2017).
Migration
With the increase in road building and modern transportation opportunities such as cheap air travel, remote areas that are burdened with endemic diseases may become more accessible to commuters (Reiter 2001). Commuters, now able to travel long distances by rail or by road to visit family or to seek medical attention may contribute to disease transmission from endemic rural areas to urban areas (Reiter 2001). Cheaper international air travel may also contribute to the distribution of disease. For instance, in the context of cholera (i.e. a food-borne and water contact disease) human mobility is incorporated in a modelling approach to determine the role of movement on the transmission dynamics of cholera (Njagarah & Nyabadza 2014; Perez-Saez et al. 2017). In the context of polio (i.e. a disease transmissible by means of respiratory contact, body fluid contact, food-borne and water contact), the international spread of the wild polio virus by means of travellers is believed to slow the global eradication of the disease (Wilder-Smith et al. 2015). Similarly, in the context of SARS (i.e. a respiratory disease), the effect of individual movement is modelled to determine the effect on the total number of infected individuals (Maeno 2016).
Socio-economic factors
Socio-economic factors are typically outside the control of the individual, however, it may affect access to healthcare and indirectly affect susceptibility to disease. Reiter (2001) states that the quality of the public health sector of numerous countries have degraded due to a lack of funding and
problems coupled with rapid urbanisation and development. For instance, the increased attention afforded in an attempt to manage AIDS prevalence in parts of Africa and South-East Asia reduced the ability of healthcare authorities to attend to other diseases. In the context of Ebola (i.e. a disease transmissible by means of direct contact, sexual contact, body fluid contact and respiratory contact) socio-economic factors are incorporated in a modelling approach to determine the effect thereof on the number of infected individuals (Sato et al. 2015). Similarly, in the context of rotavirus (i.e. a disease transmissible by means of body fluid and water contact) the potential effect of malnutrition is studied in relation to increased disease susceptibility in children (Paynter 2016).