The Emergence of Disease

in Early World-Systems:

A Theoretical Model of World-System and Pathogen Evolution[i]

 

 

 

 

Anthony Roberts

 

Department of Sociology and

Institute for Research on World-Systems (IROWS)

University of California, Riverside

 

For presentation at the annual meetings of the American Sociological Association, Atlanta, Georgia, August 15, 2010, PEWS Roundtable on the Evolution of World-Systems.  This is IROWS Working Paper #62 available at http://irows.ucr.edu/papers/irows62/irows62.htm

 

 

Draft v. 8/8/2010 Words=5,740

 

 

 

Abstract

In theorizing socio-cultural evolution, through demographic-based perspectives, a central component regulating human populations is often absent.   The model offered in this theoretical study introduces the component of disease emergence and re-emergence as a central regulator of human populations in an early horticultural society embedded in regional world-systems with hunter-gather societies.   Based on a formalized version of the Human Demographic Regulator model (Apkarian et al 2010), I incorporated an SIR disease modular into the local population system to explore how disease regulates human populations to sustainable levels for the local resources and how disease, as a regulator, interacts with inter-societal processes, emigration and warfare, to regulate human populations.   A formalized version of the modified HDR model was simulated based on different pathogen characteristics (transmission risk and mortality) to uncover population and disease dynamics.   According to the model, societies with moderate domestication rates generate the appropriate conditions for a pathogen with high virulence to sustain itself.   Further, disease operates in tandem with other regulators identified in the original HDR model, however, if the pathogen is deadly and easily transmitted, than disease becomes the primary regulator of human populations.  

 


 

In developing explanations of disease emergence, re-emergence, and spread, insufficient attention has been paid to long-term socio-historic processes as primary articulators of disease dynamics.   To compensate for this deficiency in epidemiology and other disciplines, this article seeks to incorporate basic disease dynamics in macro-structural modeling of early human societies, in particular, societies transitioning from simple sedentary societies based on diversified foraging modes of production to complex sedentary societies based on basic horticultural modes of production.   By modeling disease dynamics in simple society evolution, in roads are developed to understand how the intensification of social relations, the reduction of spatiality, and the formation of global institutions impact disease dynamics.     In a precarious period of human health, a better understanding of social systems and disease could shed light on the direction of human health in a globalizing world.

For understanding the relationship between the environment and human populations, a systemic approach is necessary to account for the mutual dependency and articulation between social and ecological systems. Social systems, as systems embedded in ecological systems, must adjust to the conditional parameters of the environment (Williams 2003; McNeil 1976; Odom 1975).   According to a Malthusian hypothesis of exponential population growth and arithmetic resource growth, there is a real ecological limit to size of human populations, thus, demography is a critical factor in the environment-human nexus.  However, over the long history of human society, social systems have increasingly pushed the boundaries of ecological constraint through expanding populations and resource intensification resulting in depletion, pollution, and other negative externalities.  

One of the answers on how social systems regulate human populations is the Iteration model (Chase-Dunn and Hall 1997, P. 102) and its formalized version, the Human Demographic Regulator (HDR) (Apkarian et al 2010).  According to the HDR model, human populations are regulated by resource limitations, warfare, and migration.   Absent in the theory, however, is the role of epidemics, which came about with the transition from simple horticulture and foraging to complex horticulture in early human societies.    This article proposes a formalized model extending from the HDR model by introducing disease dynamics as a regulator of demographic pressures on the environment.       

            Using formalized theory modeling, three variables are isolated as primary mechanism in the human-disease relationship in the HDR-disease model presented in this paper: transmission risk, mortality, and domestication (human-animal relations).    The purpose of formalizing the modified iteration model (HDR)[1] is to conceptualize how diseases became a primary regulator in early, simple sedentary societies and how disease dynamics relate to inter-societal relations between a local social system and a regional inter-societal system.   

Theoretical finding from the model indicates in social systems with moderate to high domestication, the primary regulator is the emergence and re-emergence of crowd disease[2].    Under certain conditions, specifically the presence of a developed regional system, warfare operates as a secondary regulator in control the demographic growth of local populations.   Further, a counter-cyclical trend emerges between warfare and disease, where warfare operates as a suppressant of disease reproduction in the local population.

Migration, in the modified model, is primarily driven by the rate of domestication and the virulence of the pathogens.  In resource rich scenarios with high domestication rates, population densities grow substantially, resulting in frequent waves of migration.   Under conditions with high disease lethality, however, migration never occurs because population pressures are minimized.   Given the theoretical results of the formalized model under different disease conditions, the tentative conclusion of the study is, with the emergence of domestication in sedentary societies, an intensification of the interaction of social and biological systems occurred, which results in a co-dependence between pathogens and humans.   Additionally, the synchrony of warfare and disease provides some theoretical validity to the relationship between world-systems and biological systems.  

Disease and Domestication

A central focus of the modified HDR theory is changes in human populations caused by the biological operation of pathogens and inter-societal relations in a small world-system composed of a sedentary society, with increasing population density, and a regional system of societies.    The original version of HDR developed by Apkarian et al (2010), concentrates on the relationship between human populations and local eco-systems.  In the formalized model, resource production and consumption are the core drivers of demographic dynamics in both local and regional populations.   The primary factor identified in the single society demographic regulator (SSDR), for accounting for population variation and dynamics, is consumption per capita, which is a function of both resource production and population density.  If human populations exceed the carrying capacity of the local resource system, where consumption is exceeding reproduction, than internal conflict, starvation, and other endogenous factors reduce populations to sustainable levels.    The introduction of an interacting regional system to the SSDR (WSDR) creates several more factors for regulation demographic pressure, specifically, emigration and warfare.    A mediating function for both inter-societal factors (warfare and migration) is the degree of circumscription, or the barriers to movement (Apkarian et al 2010).   

The purpose of the modified theory is to introduce a new factor in demographic dynamics, which arose out of the intensification of resource exploitation (domestication) associated with the first stage of human socio-cultural evolution – the emergence of crowd disease in sedentary societies (McNeil 1976, Ch. 2; Diamond 1997, 2002; Wolfe, Dunavan, Diamond 2007).   Besides the factors specified in the HDR model for explaining changes in populations in simple societies, the prevalence of disease is one of the main causes of morbidity and mortality in early human societies[3] (Dobson and Carper 1996).   By introducing disease into the demographic systems of the local population, the advent of domestication and the emergence of crowd disease can be theorized alongside other factors previously specified in the original HDR model.   Through modeling disease in socio-cultural evolution, form a demographic perspective, we can better understand how biological systems and human social systems operate conterminously.

The origin of the intensifying relationship between human populations and the environment is marked by several significant ruptures resulting in a transformation of the relationship.  In simple societies, the significant rupture was the transition from hunting and forging toward domestication and complex agriculture cultivation.    The rise of animal domestication and plant cultivation is associated with parallel social and demographic changes, specifically, the development of advanced technologies, expansion of human populations, and changes in ecological conditions (Cohen 1977; Flannery 1969; Diamond 1997).    Domestication of plant and animal life was an adaptive innovation of human social organization to address environmental pressures imposed by exogenous and endogenous processes from both social and ecological systems.

Prior to domestication and sedentism in human social system, the primary mode of organization and production was nomadic hunter-gathering.    In context of infection dynamics, the low population density of hunter-gathering societies was not conducive to the reproduction of disease at endemic levels (McNeil 1976, P. 53).   For disease to adapt to a host population, the rate of transmission is critical.  Low-frequency contact, due to low population densities, in host populations reduces the likelihood of transmission of virulent pathogens (Ibid, P. 21).   Further, in addition to human-human contact, nomadic hunter-gatherers had low-frequency contact with animal host populations.  Therefore, while disease existed in hunter-gathering society, the lack of transmission reduced the adaptability of pathogens to sustain itself in human host populations.   Rather large social animal populations served as better hosts for pathogens because they had the requisite population density needed for effective transmission and pathogen reproduction (Diamond 1997).    The change from nomadic hunter-gathering to sedentary domestication was the necessary change in human socio-cultural organization for pathogens to adapt to humans as host populations.

The domestication of natural resources has been associated with a variety of factors from changes in climate to changes in population density.   Gupta (2004) proposes the emergence of domestication is associated with a change in resource variability caused by climate amelioration in the early Holocene.   Favorable climate conditions, according to Gupta (2004), explains the geographic concentration of early domestication, where changes in climate increased the availability of diversified and growth of wild plants, which aided early horticulturalist in domesticating wild plants.    Diamond (1997), argues that the emergence of domestication is based on the decline of resource availability; resource depletion; technological change; and an increase in population density.    The initial cause of domestication is still under debate, but all explanations converge toward theories oriented around the human-environment relationship, rather than exogenous forces and/or moments of human innovation.   The advent of domestication was propelled by the change in the resource-human nexus.  Due to the lack of an approximate mechanism for the rise of domestication, the modified HDR model includes domestication as an exogenous variable (no other variable causes change in domestication).

In human evolution, domestication is one of the primary epochal changes in social organization and the human evolution.   By 6000 B.C., the domestication of cattle, sheep, goats, pigs, and dogs was achieved in certain human populations (Crabtree 1993).   Prior to the full-scale sedentism, a hybrid of horticultural-forging modes were common, thus, full domestication was not achieved until later in human evolution.   The intensification of animal domestication occurred 10,000 to 7,000 ago (calibrated BP years) (Gupta 2004).   For example, the domestication of cattle occurred about 7,800 years ago in present-day Turkey (Perkins 1969).   However, the introduction of domesticated animals in human societies created on powerful vector for the transmission of zoontic disease (Cockburn 1971; McNeil 1976, Ch.2).   Therefore, the mutual development of disease causing pathogens and human social organization is a parallel process in the rise of complex societies and the emergence of deadly, endemic disease is associated with major changes in human civilization and evolution.

Insight from medical anthropology contextualizes the implications of domestication in the evolutionary processes of pathogens by interpreting changes in demographic and social changes as primary drivers of disease emergence and sustainability (Armelagos and Barnes 1999;  Woolhouse and Gowtage-Sequeria  2005; Woolhouse, Haydon, and Antia 2005).    The epidemic transition associated with domestication (Armelagos, Goodman, and Jacobs 1991), created a paradoxical situation for human populations – the transition from forging and hunting modes of production to complex cultivation and animal domestication, which produced a surplus of resources to support higher human populations and greater population density, created a major transition in pathogen evolution, which caused a substantial decrease in human health. A growth in human population density created the necessary conditions for long-term survival of disease. 

However, the change in human population structures is not the only condition for the emergence of early endemic crowd diseases in human societies.    McMichael (2004) argues that the central cause for the emergence of zoonotic pathogens was the growth in animal population density accompanied with domestications.   As primary vectors, domesticated animals and mammalian-dependent animals (e.g. mosquitoes, rats, etc.), especially social animals (Diamond 1997), were highly effective transmitters of many of the zoontic diseases found in early sedentary societies (Greger 2007).   A central component in accounting for the emergence of crowd disease is the significant intensification of human-animal contact.   The domestication of wild life increased the frequency of human-animal interaction and facilitated the environmental conditions suitable for disease reproduction in animal populations (limited space, contact with waste, and several other factors).   

A modern case of pathogen and social system co-evolution is the malaria virus.  Hume, Lyon, and Day (2003) found the introduction of domestication and resulting increases in population density created the necessary conditions to sustain plasmodium falciparum in human populations.    Further, through human vectors, the spread of the parasite was primarily driven by successive waves of human migration into regional spaces.   Especially for vector-dependent transmission pathogens, like Malaria, the rise of animal domestication and plant cultivation is one of the primary factors in the emergence in parasitic and zoonotic disease (c.f. de Zulueta 1994).    Malaria provides one of the best illustrations of the dependency of human evolution and pathogen evolution began by the rise in contact between animal vectors and dense human populations.  

                Similar to the human-environment relationship, the survivability pathogen-human relationship is dependent on the ability of the pathogen to sustain its environment while transforming the environment for its survival.   In the evolution of disease strain, the balance of virulence and transmission is a primary dynamic in the sustainability and fitness of pathogens.  However, beyond the basic dynamic, there is much debate over the exact relationship between host sustainability and virulence (c.f. Alizon, Hurford, and Baalen 2009).   In general, pathogens with a high virulence are unable to reproduce at sustainable levels in the long-term because of high mortality, thus, diminishing susceptible populations for transmission.  Low mortality indicates a smaller degree of host exploitation and the risk of avirulence through immunization in host populations.   Based on the reproduction fitness of disease, the emergence of crowd disease could only be possible in with changes in human population structures, where human reproduction and immunization created endemic conditions.    This explains the lack of crowd disease in hunter-gathering societies, where population density was too low to sustain virulence over the long-term.  

             In general, the purpose of the article is to merge demographic-based theories of socio-cultural evolution, specifically, the HDR model, with disease dynamics.   The transition from hunter-gathering societies to simple horticultural societies represents a novel moment in the evolution of human societies, one marked by a central pathway in the human-environment relationship – the emergence of endemic disease.    By grafting theories of epidemic population dynamics onto the HDR model, the association between demographic dynamics, caused by disease, and social dynamics can be better understood.  More specifically, and the novel contribution of the disease-HDR model, is the formalization of the relationship between disease population dynamics and inter-societal relationships.   Based on the mechanisms and assumptions of the HDR, if early inter-societal relations were driven by demographic pressures and resources, than, disease should have a direct impact on the development of inter-societal relations.    

Disease as a Demographic Regulator

In simple sedentary societies, the degree of domestication is one of the primary factors in resource production and demographic pressures, in conjunction with natural climate and environment.    The evolutionary process of domestication signified an upward shift in the production frontier of simple sedentary societies by providing limited control over the reproduction resources in the local environment. Each upward shift, with greater technology and ability to domesticate/cultivate resources, created a stepwise development of food production (Diamond 1997).   The greater yields gained by the domesticated plants and animals facilitated the rapid population expansion in human societies, facilitating the conditions for urbanization, social complexity, and other socio-cultural changes with each new upward shift in the production frontier (Ibid).    Over the course of human social development, especially in early stages of simple societies, the degree of resource production and consumption are principle directors of demographic dynamics (Apkarian et al 2010).    Change in resource production, which creates a growth in consumption, expands the carrying capacity of local environments, allowing for larger populations.   

            As discussed earlier, the main model utilized in the theoretical study of disease dynamics and socio-cultural evolution is a formalized, mathematical model of the Human Demographic Regulator (Apkarian et al 2010; Chase-Dunn and Hall 1997).    The model is divided into two patches: a local society patch and a regional ‘societies’ patch.    Both patches are designed as modified Lotka–Volterra predator-prey models, where human populations prey on resources.    Based on Chase-Dunn and Halls’ (1997) iteration model, the HDR is a non-linear dynamic system for endogenous processes (regional population-resource systems; local population-resource systems) and exogenous processes (regional-local warfare, local-regional emigration).  

Temporal dynamics of the model are incorporated in the iterative framework, where the endogenous and exogenous processes are repeated over five thousand iterations.   The central relationships specified by the HDR model are the ‘checks’ on population pressure, specifically, internal warfare, ‘external’ warfare, and emigration.    As the local society’s population approaches carrying capacities, the system implements “checks” on the growing population to maintain an equilibrium between local resource capacity and populations.     The modified model offered in this study introduces a new endogenous check on population – disease mortality.

            The main modification to the HDR model is the application of an SIR model (Susceptible-Infectious-Recovery) to the local society patch.     The SIR model is a conventional formalization based on compartmentalizing human populations into three vectors at different stages of disease development.    Based on the differential equations developed in an earlier mathematical epidemiology study (Kermack and McKendric 1927), the key dynamic is the pathogen reproduction rate (Ro), or the number of people a single infected person will infect from the susceptible population. If R0 <1, than the disease will die out; if R0 > 1 than the disease is an epidemic; and if R0 = 1, than the disease is endemic in the host population.  Equation 1 is the rate of infection used in the model:

(1)  

Where, I is total infectious population, S is the total susceptible population, and N is the total local population.   β represents the effective contact rate formalized in Equation 2:

(2)

Where the effect contact rate is a multiplicative function of , the transmission risk, and contact rate, where p is local human population density and a is local animal human population density.

Besides the relative size of the infectious and susceptible population, the key factor in disease reproduction is the effective contact rate.   In the SIR model, effective contact is composed of two components: contact rate and transmission risk.   Contact rate is a function of human population density and animal population density, where the total contact rate is the summation of the probability of human-human contact and animal-human contact rate.  

Transmission risk is an exogenous factor, which varies from .02 to and 1, where .02 is a two percent chance of transmission and 1 is a 100 percent chance of transmission.   The purpose of leaving transmission risk an exogenous factor is to retain the capability to adjust the model to specific pathogen strains.   Transmission rates vary depending on the nature of transmissions.  For example, frequency dependent diseases, such as STDs, vary in transmission risk depending on the behavior of the social actor (Boots and Sasaki 2003).

An important assumption in the SIR modular is that the recovered population gains immunity to the disease through contraction and recovery.   Another assumption of model is there is no cross-generation immunization, where immune parents pass on immunity to children, rather, the only way to gain immunity is to be exposed and infected with the disease.    Immunization plays a critical role in human development.  Further, the model does not incorporate any genetic adaptation to the disease.   Immunity to deadly pathogens has played a central role in the expansion of European populations across the globe (Diamond 1997), thus, representing a key component in human socio-cultural evolution. However, due to the extreme complexity associated with immunization and other genetic adaptation to diseases, the model does not account for genetic changes in the local population.  

            Disease mortality in the model is an exogenous factor and a probabilistic function of consumption per capita.    High consumption is assumed to translate into healthier populations, which causes an increase of 20% in the rate of recovery from the disease.    Normal recovery rate, or virulence, is an exogenous variable ranging from 0% to 100%.    The purpose of not endogenizing virulence is to all for tests of different strains with greater or less mortality.  

            In addition to the SIR modular, the modified HDR model incorporates domestication rate as an exogenous factor influencing animal population density, resource reproduction, and harvesting rate.   The purpose is to model the upward shift in resource production due to the rate of resources domesticated by the local population.    Due to the ambiguity of the casual processes for domestication, the variable is left exogenous to model.   Indirectly, domestication relates to disease dynamics through increasing the contact rate (increasing animal population density and human population density).   Human population densities increase with greater resource production and increasing birth rates.   Animal population densities are modeled as the proportion of fauna in the local eco-system domesticated.   An increase in domestication is associated with a higher frequency of contact between host populations (both human and animal).

            No modifications were made to the regional patch in the HDR model in order to retain core-periphery differentiation[4] between the complex local sedentary society (core) and simple regional sedentary societies (periphery).   The only indicator of core-periphery difference is resource consumption and production capacity since the regional system is modeled as an aggregated system of simple, horticultural societies, which renders population density indicators invalid (unless one assumes the number of societies in the regional system).  Resource production and consumption is greater in local societies with a moderate rate of domestication.    

            Inter-societal relations, in the HDR model, only occur through emigration and warfare, thus, a core assumption of the model is no positive relations.   In context of the modified HDR model, the demographic dynamics of the local society operates as a mediator in the relationship between disease and inter-societal relations.   According to the HDR model, the primary factor driving warfare is the relative the degree of circumscription, which is a function of regional population and resource consumption.    The main effect of disease on inter-societal relations is the dampening effecting on emigration, where virulence operates as a check on demographic pressure.    Further, domestication increasingly improves consumption per capita, limiting the effect of internal conflict on emigration.    The expected relationship between local disease dynamics, domestication, and warfare is a negative association.  The primary justification for the association is centered on the demography of warfare and conflict in early world-systems.   Population pressure over scarce resources results in higher frequencies of internal conflict and emigration (Chase-Dunn and Hall 1997).  However, in conditions with low population pressure, due to the emergence of lethal pathogens, other regulations would function as secondary mechanisms.   Therefore, in conditions with small populations distributed over sufficient space, conflict is unlikely to arise at the local or systemic level. 

            Three parameters are elucidated in the analysis of the relationship between disease characteristics and inter-societal relations: transmission risk, mortality, and domestication.   Disease, migration, and warfare are theorized as central mechanisms in the regulation of human populations in early simple societies.   In the transition to more complex, agricultural societies, I argue that diseases becomes the primary demographic regulator until the society develops endogenous and exogenous means of handling disease (advanced health and medicine; sanitation; immunization; etc.).   Scenarios with disease as the primary regulator, I predict little to no warfare or migration will occur, which will result in little regional formation.  

Modified HDR Model

The methodological objective of this theoretical study is the development of a mathematical formalization of the modified HDR model.    Using the single society and world-system versions of the HDR model (c.f. Apkarian 2010 et al), I added a generic SIR modular.[5] Further, an additional modification to the HDR model is the introduction of domestication and animal population density.    Domestication is an exogenous rate with the range of 0 to 1, where 1 indicates that most, if not all, resources are locally cultivated in a specific space.   Domestication influences three components of the modified HDR model.  First, domestication causes an upward shift in the local resource reproduction rate.   Second, domestication increases the local harvesting rate of available resources.  Lastly, domestication increases the animal population density.    The function for animal population density is the following:

(3) 

Where, a is animal population density; r is the available local resources; l is the local land size; and d is the domestication rate.

The assumptions governing animal population density is that the eco-system is composed half fauna and half flora.    Domestication operates as the percentage of local fauna that lives in close proximity to human populations based on the spatial scale of the society (land).   According to these relations, domestication plays a central role in reproducing human societies (increasing access to greater resources and consumptions), while at the same time, creating the conditions for the emergence of crowd disease (Diamond 1997).   The capture and domestication of wild animals facilitated the emergence of disease pools where pathogens, adapted to animal host populations, were able to transmit to densely populated human societies.   

The purpose of the formalized model is to explore the hypothetical relationship between disease and inter-societal relations as demographic regulators.  In order to test this relationship between disease and inter-societal relations, I performed a series of simulations using a formalized version of the HDR-disease model developed in STELLA 9.0.2[6] (iseeSystems 2007).   In total, ran 135 simulations based on 27 scenarios.   Each scenario was estimated 5 times in order to adjust for stochastic parameters in the HDR model.    Three parameters defined the different scenarios: transmission risk, mortality, and domestication.   Each parameter was assigned a low, medium, and high value.  For transmission risk, the low value was .02, the medium value was .5, and the high value was 1.    For mortality, low value was .05, the medium value was .5, and then high value is .9.    Lastly, domestication ranged from the low value, 0, to the high value 1.   The purpose of the different settings was to test the sensitivity of the model to changes in pathogen strain.   Certain pathogens, such as influenza, are highly contagious and transmission is high in dense populations, while other diseases, such as HIV, have lower transmission risk, but higher mortality.   Further, outside of pathogens, different rate of domestication is used as an indicator of how variation in resource production, as a result of human domestication, impacts the dynamics of disease and inter-societal relations.

Model Dynamics:  Disease and Inter-Societal Relations

Disease

The results of the simulations confirmed the expected relationship between disease, domestication, warfare, and migration.  In scenarios with medium domestication, where the proportion of resource production exceeds 50% and local pathogens are highly virulent and transmission risk is moderate to high, disease has a high risk of causing the local population to go extinct.   If probability of mortality is less than 50%, the local population is regulated by disease mortality.     In conditions with disease regulation, human populations experience a cyclical trend in emergence and re-emergence of disease.   Over the course of 2000 iterations, the infection reproduction rate oscillates between near-zero to about 100.  Figure 3 (in Appendix) shows the cyclical patterning of the number of infections per iteration.   A general interpretation of the cyclical trending is the emergence of an epidemic, followed by a series of endemic iterations, and re-emergence of epidemic.   This type of cyclical dynamic is expected in early populations without genetic adaptations for immunity to certain endemic disease[7].

Warfare and Disease 

A central assumption in the original HDR model is that the regional area is depopulated and can only be populated by migration from the local population.   In order to test the model’s war dynamics, I set the initial regional population to ten times that of the local population.   When the regional system is populated, over successive iterations, warfare emerges as a secondary regulator of local populations.   Setting the disease conditions to relatively medium and high conditions (transmission risk = .7; mortality =.17) and moderate domestication (50% of resource production), the emergence of disease and the cyclical trending of emergence and re-emergence, is synchronized with the cycles of warfare.   In iterations with the presence of disease and warfare, warfare operates as a suppressant of the disease reproduction rate (bringing R0 to <1 conditions for the iteration).   Figure 4 (in the Appendix) shows the cyclical relationship between warfare and disease reproduction rate under favorable conditions for disease emergence.   In context of Figure 3, the counter-cyclical relationship appears to be one of second-order regulation, where warfare, through death, operates regulates the spread of disease. In iterations with warfare, the infection rate lessens in magnitude, but maintains epidemic status.    The interaction between these two dynamics illustrates the growing complexity of demographic regulation in human societies, however, warfare is assumed to be secondary to disease in regulating human populations to sustainable levels.

Migration and Disease

A key dynamic in the relationship between regional and local population is the migration of local populations into regional space.  According to Hume et al (2003), early migration and spread of disease was a function of overcrowding caused by sedentary lifestyles, urbanization, and domestication.  Under normal operating conditions, the HDR model holds initial regional populations at zero and, over successive iteration, the local population migrates into the regional system.   

The introduction of domestication into the local society patch changes the primary drivers of migration in the society.   Low to medium domestication rates in the modified HDR model produces no migration because the outward shift in resource production caused by domestication diminishes the likelihood of internal conflict and raises consumption per capita.  As long as diseases are present in the population, populations never exceed the critical population density of two people per unit of land because disease death operates as a regulator.   In the modified model, once the population exceeds this capacity, than migration increases.   Once migration occurs and the regional system populates, war dynamics tend to co-determine populations with disease and other regulators.

            Under the same disease conditions simulated in the warfare tests, migration tests were performed to observe the theoretical relationship between disease, migration, and domestication.     Scenarios with high pathogen virulence (high transmission risk and low mortality) and little to no domestication, diseases emerge very sporadically in the local population, and migration occurs early in the simulation (prior to 500 years).   In high virulence conditions with low to moderate domestication, migration is unlikely to occur.    Resource consumption in the local society never results in the critical population density, thus, ecological needs are met and overcrowding never occurs.   Once domestication rates exceed .75, migration occurs rapidly and frequent as a result of overcrowding.    Figure 5 (in Appendix) shows the cyclical patterns in migration, warfare, and disease reproduction.    No real synchrony emerges between the different regulators because each are functional and operative in each iteration.   In scenarios where all three regulators (disease, migration, and warfare) are present, local and regional populations has severe variation around a general equilibrium of about 850 (regional) and 150 (local).   Figure 6 (in Appendix) shows the severe oscillation of populations over 2000 iterations.      

            In scenarios where the disease is assumed to have high to moderate mortality, migration never occurs because disease death assumes the role of primary demographic regulator.   Besides domestication, migration is highly dependent on the virulent mortality since migration is a function of consumption, internal conflict, and population density in the HDR model.   If disease is regulating the local population through high mortality, the regulatory function of migration is unnecessary, thus, the world-system (local system plus regional population) remains unpopulated.   Only in scenarios where the local population doesn’t experience high virulent mortality, regional systems begin developing and disease becomes endemic or follows a cyclical patterns of emergence and re-emergence. 

            Unlike warfare, migration in the model does not follow a systemic pattern with disease dynamics.    Rather, in scenarios with high population density from domestication and moderate to high virulence, migration happens at a higher frequency than disease dynamics and warfare.   In this scenario, migration becomes the primary demographic regulator and disease and warfare become secondary.   

Discussion and Conclusion

The purpose of extending on previous mathematical formalization of human social evolution is to introduce disease into the theorization of long-term systemic evolution.   The role of disease in early human society, and with the advent of domestication, has been central in regulating the relationship between human populations and their eco-system.    At the nexus of humans-ecology, pathogens operate as environmental responses to human action and represent a biological systems being co-determined by the interaction between society and the environment.   Using simulation techniques and mathematical formalizations, I developed a simple addition to the HDR model aimed at testing the human, system, and disease dynamics in scenarios of different pathogen conditions.  

The main emphasis has been on the relationship between disease and inter-societal relations, specifically, warfare and migration.   The outcome of the model suggests that disease, in early sedentary societies, functioned as a secondary and primary regulator of human populations.   Beyond resource consumption, the roles for migration, inter-societal conflict, and disease are centered on maintaining an equilibrium between human populations and local resources.    Previous theorization (Apkarian et al 2010) using the HDR model, found that endogenous (resource consumption) and exogenous processes (war and migration) operated to produced equilibrium solutions with variation.   Under certain conditions, the domestication and pathogen virulence created similar outcomes, but variation and equilibrium was dictated by disease dynamics or disease killed the populations.

The next step in the modeling process is to include endogenous processes of pathogen adaptation to human host populations.  Over the course of co-dependent evolution and human social evolution, pathogens have becoming increasingly apt at responding to changes in their ecosystems, i.e. human populations.   Further, the emergence of immunization, as both an adaptation of disease to human and human to disease needs to be included in order to understand how pathogens have maintained an endemic status in human societies.   Lastly, climate variation needs to be included in order to account for the early emergence of temperature-dependent microorganism.   

To account for the inter-societal dimension of disease, the next model will include the process of diffusion between two societies, where migrants of the local population are able to spread diseases into surrounding populations.   The inter-societal transmission of disease is an important factor in understanding the next major epidemic transition: pandemics.   The intensification of inter-societal connections in the modern world-system can be traced to earlier mergers of disease pools in Eurasia (McNeil 1976, Ch.3).   The spread of disease across world-systems is an important component to the story of human social evolution and, in contemporary times, remains a central concern for human health and development.

References

 

Alizon,S., A. Hurford, and M. Van Baalen.  2009.  “Virulence Evolution and the Trade-Off

Hypothesis: History, Current State of Affairs and the Future.”  Journal of Evolutionary Biology 22: 245-259.

Apkarian, Jacob, Jesse Fletcher, Robert A. Hanneman, Hiroko Inoue, Kirk Lawrence and

Christopher Chase-Dunn. 2010.   “Human Demographic Regulators in Small-Scale World-Systems.” IROWS Working Paper 41.

Armelagos, George, A.H. Goodman, and K. Jacobs.  1991.  “The Origins of Agriculture:

Population Growth during a Period of Declining Health.” Population and Environment 13(l):9-22.

Armelagos, George and Kathleen Barnes.  1999.  “The Evolution of Human Disease and the Rise

of Allergy: Epidemiological Transitions.”  Medical Anthropology 18: 187-213.

Chase-Dunn, Christopher Thomas Hall.  1997.  Rise and Demise: Comparing World-Systems. 

Boulder: Westview Press.

Cockburn, T.A. 1971.  “Infectious Disease in Ancient Populations.” Current Anthropology

12(1):45-62.

Cohen, Mark. 1977. The Food Crisis in Prehistory: Overpopulation and the Origins of

Agriculture. New Haven: Yale University Press.

Crabtree, Pam.  1993.  “Early Animal Domestication in the Middle East and Europe.”

Archaeological Theory and Method 5: 201-245.

de Zulueta, J. 1994.  “Malaria and Ecosystems: From Prehistory to Posteradication.”

Parassitologia 36:  7–15.

Diamond, Jared.  1997.  Guns, Germs, and Steel: The Fates of Human Societies.  New York: W.

W. Norton & Company.

_______.  2002.  “Evolution, Consequences and Future of Plant and Animal Domestication.”

Nature 418: 700-707.

Dobson, Andrew and E. Robin Carper.  1996.  “Infectious Diseases and Human Population

History.”   Bioscience 46(2): 115-125.

Flannery, K. 1969.  “Origins and the Ecological Effects of Early Domestication in Iran and the

Near East.”  Pp. 73-100 in The Domestication and Exploitation of Plants and Animals, eds. Ucko, P. and G.W. Dimbleby.

Greger, Michael.   2007.  “The Human/Animal Interface: Emergence and Resurgence of

Zoonotic Infectious Diseases.”  Critical Reviews in Microbiology 33: 243-299.

Gupta, Anil.  2004.  “Origin of Agriculture and Domestication of Plants and Animals Linked to

Early Holocene Climate Amelioration.”  Current Science 87(1): 54-59.

Hannon, Bruce and Matthias Ruth.  2009.  "Basic Epidemic Models."  In Dynamic Modeling of

Diseases and Pests.  New York, NY: Springer.

Hume, Jennifer, Emily Lyon, and Karen Day.  2003.  “Human Migration, Mosquitoes and the

Evolution of Plasmodium Falciparum.”  Trends in Parasitology 19(3): 144-149.

isee Systems. 2007. STELLA 9.0.2. Lebanon, NH: isee Systems

Kermack, W.O. and A.G. McKendrick.  1927.  "A Contribution to the Mathematical Theory of

Epidemics."   Proceedings of the Royal Society of London. Series A, Containing Papers

of a Mathematical and Physical Character, 115 (772):  700-721.

McMichael, A.J.  2004.  “Environmental and Social Influences on Emerging Infectious Diseases:

Past, Present and Future.”   Philosophical Transactions of the Royal Society B 359: 1049-1058.

McNeil, William.  1976.  Plagues and Peoples.  New York: Anchor Books.

Odum, Eugene.  1975. Ecology: The Link Between the Natural and Social Sciences, 2nd Edition.

New York: Holt-Saunders.

Perkins, Drexter.  1969.  “Fauna of Catal Huyuk: Evidence for Early Cattle Domestication in

Anatolia.”   Science 164:177-179.

Williams, Michael. 2003. Deforesting the Earth: From Prehistory to Global Crisis.

Chicago: University of Chicago Press.

Wolfe, Nathan, Claire Dunavan, and Jared Diamond.  2007.   “Origins of Major Human

Infectious Diseases.”  Nature 447: 297-283.

Woolhouse Mark, Daniel Haydon, and Rustom Antia.  2005.  “Emerging Pathogens: the

Epidemiology and Evolution of Species Jumps.”   Trends in Ecology and Evolution 20(5): 238-244.

Woolhouse, Mark and Sonya Gowtage-Sequeria.  2005.  Host Range and Emerging and         Reemerging Pathogens.”  Emerging Infectious Diseases 11 (2): 1842-1847.

 


 

Appendix

 

Figure 1.   Iteration Model with Epidemics

 

Figure 2.  SIR Modular

Figure 3.  Cyclical Epidemic-Endemic Trends in Local Population

 

Figure 4.  Synchrony of Warfare and Infection Rate

 

 

 

 

Figure 5. Demographic Regulators

 

Figure 6.   Regional and Local Population



[1] See Appendix, Figure 1.

[2] Crowd diseases are defined as epidemics with a high rate of infection; acute illness; post-disease immunization; and, over time, primarily infect humans( Diamond 1997, Pp. 202-205).   Based on these characteristics, I assume they can only exist in human societies with dense populations capable of hosting disease-causing pathogens.   Hunter-gathering societies would have been unable to sustain the high virulence of crowd diseases.  

[3] McNeil (1976, Ch. 1) argues that parasites and other infectious pathogens served as an ecological check for early human populations.   However, the likelihood of infection contraction and pathogen sustainability in human populations must have been low in early human societies due to the low population-density and lack of immunization.   An outbreak of infection (severe epidemic) in early hunter-gatherer societies would have been likely to decimate the population to unrecoverable levels. 

[4] Chase-Dunn and Hall 1997, P.  36.

[5] See Appendix, Figure 2. 

[6] For a review of using STELLA to model basic disease dynamics see Hannon and Ruth (2009, Ch. 2). 

[7] McNeil (1976, P.51-52) considers the cyclical trending to be a stabilization of disease regulation in human host populations.   In these cyclical trends, fluctuations are interpreted as recurrent epidemic episodes.



[i] Working draft manuscript.   Please direct all correspondence to Anthony Roberts, Department of Sociology, University of California, Riverside 2116 Watkins Hall, Riverside, CA. 92521.  anthony.roberts@ucr.edu