Individual-based Modeling Approach Research Articles

Alternative reproductive tactics and evolutionary rescue.

Almost all life on earth is facing environmental change, and understanding how populations will respond to these changes is of urgent importance. One factor that is known to affect the speed by which a population can evolve when faced with changes in the environment is strong sexual selection. This increases the adaptive capacity of a population by increasing reproductive skew toward well-adapted (usually) males who will, on average, be best able to compete for matings. This effect could potentially be disrupted when males pursue alternative reproductive tactics (ARTs), whereby males within a species exhibit qualitatively different behaviors in their pursuit of matings. ARTs are diverse, but one common class is those expressed through condition-dependent polyphenism such that high-quality, well-adapted males compete aggressively for mates and low-quality, poorly adapted males attempt to acquire matings via other, nonaggressive behaviors. Here, using an individual-based modeling approach, we consider the possible impacts of ARTs on adaptation and evolutionary rescue. When the ART is simultaneous, meaning that low-quality males not only engage in contests but also pursue other tactics, adaptive capacity is reduced and evolutionary rescue, where a population avoids extinction by adapting to a changing environment, becomes less likely. This is because the use of the ART allows low-quality males to contribute more maladaptive genes to the population than would happen otherwise. When the ART is fixed, however, such that low-quality males will only use the alternative tactic and do not engage in contests, we find the opposite: adaptation happens more quickly and evolutionary rescue when the environment changes is more likely. This surprising effect is caused by an increase in the mating success of the highest quality males who face many fewer competitors in this scenario-counterintuitively, the presence of males pursuing the ART increases reproductive skew toward those males in the best condition.

The construction of a measure of behavioural complexity as a potential individual-based welfare indicator and its theoretical validation

Abstract Behavioural complexity is likely to reflect how animals cope with their environment. A large behavioural repertoire and the ability to flexibly apply these behaviours provide an individual with a greater likelihood of adapting to its (captive) environment. Here, we developed a procedure to aggregate different behavioural measures into a condensed measure of behavioural complexity based on 14 features, which were previously proposed (e.g. number of behaviours, Shannon diversity index) as well as some new features of behavioural complexity (e.g. variances of within and between transition durations). To test the measure, artificial behavioural sequences with potentially varying complexity were created using an individual-based modelling approach. With a Principal Component Analysis, the features extracted from these sequences could be reduced to two components (‘general complexity’ and ‘state variability’) explaining 59.64 and 27.68% of the total variance, respectively. The effect of the aspects of the artificial behavioural sequences on ‘general complexity’ and ‘transitions variability’ were analysed using linear mixed-effects models. The number of behavioural categories and the proportion of short behavioural states had the largest effect on the components. Both components were increasing with a greater number of behavioural categories, whereas the proportion of short behavioural states the opposite effect on the components. We also tested the approach on real data-sets. The principle components were not identical to the ones from the simulation. Yet, we found consistencies and similarities in the loadings. The approach for an aggregated measure of behavioural complexity developed here could form the basis of an individual-based animal welfare indicator for intensively kept animals.

Exploring fishing impacts on the structure and functioning of the Yellow Sea ecosystem using an individual-based modeling approach

The Yellow Sea is a marginal sea in the Northwestern Pacific where the fishery resources have been overfished and the community structure has greatly changed over the past six decades. Ecosystem modeling approaches are valuable tools to uncover potential mechanisms behind the ecosystem changes. Here, we developed ‘OSMOSE-YS’, an individual-based multi-species OSMOSE model that includes important commercial pelagic and demersal fish and invertebrates in the Yellow Sea. Simulations were carried out under three fishing scenarios to investigate how different levels of fishing pressure may have impacted the Yellow Sea ecosystem. Results indicate that the biomass of demersal fish continued to decline during 1970–2014, while the biomass of pelagic fish and invertebrates fluctuated periodically. Long-term fishing pressure has led to the reduction of total biomass, body sizes, and longevity of the modelled species. Under low-fishing condition, the ecosystem biomass is restored and the proportion of elder and larger individuals increases. On the contrary, high-fishing condition further decreases the proportion of high-trophic-level species. OSMOSE-YS serves as a baseline model to investigate ecosystem responses to different fishing strategies, in support of ecosystem-based fisheries management in the Yellow Sea.

A demo-genetic model shows how silviculture reduces natural density-dependent selection in tree populations.

Biological production systems and conservation programs benefit from and should care for evolutionary processes. Developing evolution-oriented strategies requires knowledge of the evolutionary consequences of management across timescales. Here, we used an individual-based demo-genetic modelling approach to study the interactions and feedback between tree thinning, genetic evolution, and forest stand dynamics. The model combines processes that jointly drive survival and mating success-tree growth, competition and regeneration-with genetic variation of quantitative traits related to these processes. In various management and disturbance scenarios, the evolutionary rates predicted by the coupled demo-genetic model for a growth-related trait, vigor, fit within the range of empirical estimates found in the literature for wild plant and animal populations. We used this model to simulate non-selective silviculture and disturbance scenarios over four generations of trees. We characterized and quantified the effect of thinning frequencies and intensities and length of the management cycle on viability selection driven by competition and fecundity selection. The thinning regimes had a drastic long-term effect on the evolutionary rate of vigor over generations, potentially reaching 84% reduction, depending on management intensity, cycle length and disturbance regime. The reduction of genetic variance by viability selection within each generation was driven by changes in genotypic frequencies rather than by gene diversity, resulting in low-long-term erosion of the variance across generations, despite short-term fluctuations within generations. The comparison among silviculture and disturbance scenarios was qualitatively robust to assumptions on the genetic architecture of the trait. Thus, the evolutionary consequences of management result from the interference between human interventions and natural evolutionary processes. Non-selective thinning, as considered here, reduces the intensity of natural selection, while selective thinning (on tree size or other criteria) might reduce or reinforce it depending on the forester's tree choice and thinning intensity.

Numerical and experimental investigation of multi-species bacterial co-aggregation

This paper deals with the mathematical modeling of bacterial co-aggregation and its numerical implementation in a FEM framework. Since the concept of co-aggregation refers to the physical binding between cells of different microbial species, a system composed of two species is considered in the modeling framework. The extension of the model to an arbitrary number of species is straightforward. In addition to two-species (multi-species growth) dynamics, the transport of a nutritional substance and the extent of co-aggregation are introduced into the model as the third and fourth primary variables. A phase-field modeling approach is employed to describe the co-aggregation between the two species. The mathematical model is three-dimensional and fully based on the continuum description of the problem without any need for discrete agents which are the key elements of the individual-based modeling approach. It is shown that the use of a phase-field-based model is equivalent to a particular form of classical diffusion-reaction systems. Unlike the so-called mixture models, the evolution of each component of the multi-species system is captured thanks to the inherent capability of phase-field modeling in treating systems consisting of distinct multi-phases. The details of numerical implementation in a FEM framework are also presented. Indeed, a new multi-field user element is developed and implemented in ANSYS for this multiphysics problem. Predictions of the model are compared with the experimental observations. By that, the versatility and applicability of the model and the numerical tool are well established.

Collective cell migration due to guidance-by-followers is robust to multiple stimuli

Collective cell migration is an important process during biological development and tissue repair but may turn malignant during tumor invasion. Mathematical and computational models are essential to unravel the mechanisms of self-organization that underlie the emergence of collective migration from the interactions among individual cells. Recently, guidance-by-followers was identified as one such underlying mechanism of collective cell migration in the embryo of the zebrafish. This poses the question of how the guidance stimuli are integrated when multiple cells interact simultaneously. In this study, we extend a recent individual-based model by an integration step of the vectorial guidance stimuli and compare model predictions obtained for different variants of the mechanism (arithmetic mean of stimuli, dominance of stimulus with largest transmission interface, and dominance of most head-on stimulus). Simulations are carried out and quantified within the modeling and simulation framework Morpheus. Collective cell migration is found to be robust and qualitatively identical for all considered variants of stimulus integration. Moreover, this study highlights the role of individual-based modeling approaches for understanding collective phenomena at the population scale that emerge from cell-cell interactions.

Mathematical modelling of gene delivery in patients with haemophilia B

Haemophilia B is a bleeding disorder resulting from a deficiency of coagulation factor IX (FIX). Although gene therapy is a potentially curative treatment option, optimising the dosing of therapeutic genes for patients remains a challenge. Detailed simulation of gene delivery systems is required for an improved understanding of the system. Hence, the purpose of this paper is to develop a modelling framework to predict the physiological response of a subject affected by type B haemophilia to a dose of the vector. To address this, an integrated pharmacokinetic/pharmacodynamic (PK/PD) modelling platform was developed based on in vivo clinical data for three patients with severe haemophilia B, whose functional plasma levels of FIX are less than 1% of the normal value. The plasma FIX activity was considered as the pharmacological effect, while the level of serum alanine aminotransferase (ALT) demonstrated the hepatocellular toxicity. Both an individual-based modelling approach and a population modelling approach were used to estimate the physiological parameters of the developed PK/PD models. The models were then validated using data from the clinical study before being used in a simulation-based modelling approach to provide dosing recommendations. The results obtained from the study demonstrate a good prediction of the pharmacokinetics and pharmacodynamics of the vector. Model-based simulations were subsequently performed to guide initial dose selection to provide clinicians with better tools to simplify the decision-making process for designing more effective treatment plans.

Modelling the management of an invasive species at landscape scale: are oral contraceptives the missing ingredient for success?

Context Invasive non-native species are on the rise worldwide, exacerbating already significant environmental and economic impacts. Concurrently, public attitudes towards methods of controlling these species are changing, with greater demand for non-lethal solutions. This has fostered research into developing new, effective technologies and strategies for wildlife management. Aims On the basis of a case study focusing on the eastern grey squirrel (Sciurus carolinensis) in the UK, this study aimed to understand the potential for population management, using either live trapping or oral contraception. First, assuming completely coordinated landscape-scale control, and then, coordinated control only on parts of the landscape, accounting for the potential that landowners’ attitudes towards alternative methods may differ and reflect those of the wider public. Methods We used an existing spatially explicit individual-based modelling approach applying various parameterisations to define management. We varied the density of traps or hoppers delivering contraceptives, the duration of deployment, contraceptive efficacy, initial population size, and the probability of landowner participation. The latter was based on a previous public survey, suggesting that 40% of the respondents were open to management using trapping and 64% to using contraception. Key results With complete coordinated control, trapping was generally faster and more cost-effective than was contraception. However, when differences in social acceptance were considered, reducing participation and, consequently, the spatial coverage of management, contraception was found to maintain greater population reductions than was trapping with similar effort, assuming a contraceptive efficacy of 75% or higher. Conclusions This study added another layer of complexity to managing invasive non-native species, namely the potential effect that landowners’ attitudes to different methods of population management might have on the level of coordinated control at landscape scale. In a situation such as the one modelled by this study, where management is not a legal requirement, this human dimension must be considered alongside cost-effectiveness, to develop successful control in line with management goals. Implications Further work is required to establish the actual attitudes of landowners, and in different contexts (e.g. urban, rural areas), how this may change as new approaches become available, and then how spatial variation (clustering) in the resulting wildlife population control may affect outcomes.

Grizzly bear habitat selection across the Northern Continental Divide Ecosystem

Understanding habitat selection is challenging but key for species of conservation concern, including grizzly bears (Ursus arctos). Here we demonstrate an approach for understanding and predicting habitat use over multiple stages that test hypotheses of animal behavior, use newly gained knowledge to mechanistically simulate individual movements, translate results to predictive habitat maps, and test their predictive power across a large spatiotemporal scale. Grizzly bears in the Northern Continental Divide Ecosystem of northwest Montana served as our study system. Mechanistically modeling grizzly bear movements demonstrated that grizzly bears have highly individualistic spatial behaviors. Some individuals avoided whereas others preferred areas of vegetation green-up, terrain ruggedness, forest edge, riparian areas, building densities, and secure habitat. Such individualism supported the need for an individual-based modeling approach to understand and predict grizzly bear behavior. External validation using >375,000 GPS locations for 261 individuals over nearly 2 decades demonstrated mean Spearman rank scores of >0.90 across seasons and years, and overall scores of 1.0. The top 5 classes of our predictive habitat maps contained 73.5 % of female fixes and 83.6 % of male fixes, and the top class (comprising 10 % of mapped area) contained 25.6 % and 41.7 % of female and male fixes, respectively. Results of this research provide tools for conservation planning and serve as the basis for future grizzly bear research within our study system and beyond. Our multi-stage approach for understanding and predicting habitat use has high utility for conservation of myriad threatened species around the globe.

Predicting point-source invasion success in the Queensland fruit fly (Bactrocera tryoni): An individual-based modelling approach

Fruit flies are a major pest worldwide for fruit producing industries. Incursions into fly free areas usually occurs via infested fruit; however, little is reported about the size of a propagule to start a new establishment. Propagule pressure is an important predictor of the probability that an invasive species will establish a breeding population. Recently, individual-based model (IBM) became a valuable modelling tool for exploring invasive species. Here, we developed a spatially-explicit individual-based model (IBM) specifically parameterized for the Queensland fruit fly (Qfly) (Bactrocera tryoni Froggatt), a major horticultural pest species in Australia and a major quarantine concern worldwide. Usually, new establishments or incursions originate from the introduction of infested fruit and larvae must progress through several stages to become sexually mature adults. For a Qfly incursion to establish a breeding population, flies must avoid predators and survive long enough to become reproductively mature, find a mate, and find ripening fruit. Additionally, their offspring must survive and successfully reproduce. The IBM model simulated release of a fixed number of flies into a heterogeneous environment of varying suitability. We conducted simulations to delineate the contributions of demographic stochasticity and Allee effects (finding a mate in a spatial environment) on establishment probability. Our model delineated several key influential factors, especially tree density, extrinsic mortality and mating range. We performed sensitivity analyses to identity which biological and ecological mechanisms strongly affect establishment probability. Integrating the model's findings with information on transport of infested fruit helps explain why Qfly incursions are common, but yet most fail to establish. Our model predicted that circa 16 and 50 flies were required for a 50% and 99% chance of establishment respectively. Additionally, our model predicted the mating distance for Qfly was 16.1 m. The mean daily dispersal distance was 30 m. We discuss how this knowledge might be used to better manage Qfly by alterations in surveillance and male annihilation technique.

Individual-based modeling reveals that the COVID-19 isolation period can be shortened by community vaccination

The isolation of infected individuals and quarantine of their contacts are usually employed to mitigate the transmission of SARS-CoV-2. Although 14-day isolation of infected individuals could effectively reduce the risk of subsequent transmission, it also substantially impacts the patient's psychological and emotional well-being. It is, therefore, vital to investigate how the isolation duration could be shortened when effective vaccines are available. Here, an individual-based modeling approach was employed to estimate the likelihood of secondary infections and the likelihood of an outbreak following the isolation of a primary case for a range of isolation periods. Our individual-based model integrated the viral loads and infectiousness profiles of vaccinated and unvaccinated infected individuals. The effects of waning vaccine-induced immunity against infection were also considered. By simulating the transmission of the SARS-CoV-2 Delta (B.1.617.2) variant in a community, we found that in the baseline scenario in which all individuals were unvaccinated and nonpharmaceutical interventions were not used, there was an approximately 3% chance that an unvaccinated individual would lead to at least one secondary infection after being isolated for 14 days, and a sustained chain of transmission could occur with a less than 1% chance. With the outbreak risk equivalent to that of the 14-day isolation in the baseline scenario, we found that the isolation duration could be shortened to 7.33 days (95% CI 6.68–7.98) if 75% of people in the community were fully vaccinated with the BNT162b2 vaccine within the last three months. In the best-case scenario in which all individuals in the community are fully vaccinated, isolation of Delta variant-infected individuals may no longer be necessary. However, to keep the outbreak risk lower than 1%, a booster vaccination may be necessary three months after full vaccination.

The effect of brown kelp phenology on abalone locomotion and spatial distribution: acoustic telemetry and spatially explicit individual-based model approach

The effect of brown kelp phenology on abalone locomotion and spatial distribution: acoustic telemetry and spatially explicit individual-based model approach

DNA metabarcoding suggests adaptive seasonal variation of individual trophic traits in a critically endangered fish.

Dietary studies are critical for understanding foraging strategies and have important applications in conservation and habitat management. We applied a robust metabarcoding protocol to characterize the diet of the critically endangered freshwater fish Zingel asper (the Rhone streber). We conducted modelling and simulation analyses to identify and characterize some of the drivers of individual trophic trait variation in this species. We found that population density and ontogeny had minor effects on the trophic niche of Z.asper. Instead, our results suggest that the majority of trophic niche variation was driven by seasonal variation in ecological opportunity. The total trophic niche width of Z.asper seasonally expanded to include a broader range of prey. Furthermore, null model simulations revealed that the increase of between-individual variation in autumn indicates that Z.asper become more opportunistic relative to summer and spring, rather than being associated with a seasonal specialization of individuals. Overall, our results suggest an adaptive variation of individual trophic traits in Z.asper: the species mainly consumes a few ephemeropteran taxa (Baetis fuscatus and Ecdyonurus) but seems to be capable of adapting its foraging strategy to maintain its body condition. This study illustrates how metabarcoding data obtained from faeces can be validated and combined with individual-based modelling and simulation approaches to explore inter- and intrapopulational individual trophic traits variation and to test hypotheses in the conventional analytic framework of trophic ecology.

Optimal management of stochastic invasion in a metapopulation with Allee effects

Invasive species account for incalculable damages worldwide, in both ecological and bioeconomic terms. The question of how a network of invasive populations can be optimally managed is one that deserves further exploration. A study accounting for partial observability and imperfect detection, in particular, could yield useful insights into species eradication efforts. Here, we generalized a simple model system that we developed in previous work. This model consists of three interacting populations with underlying strong Allee effects and stochastic dynamics, inhabiting distinct locations connected by dispersal, which can generate bistability. To explore the stochastic dynamics, we formulated an individual-based modeling approach. Next, using the theory of continuous-time Markov chains, we approximated the original high-dimensional model by a Markov chain with eight states, with each state corresponding to a combination of population thresholds. We then used the reduced model as the core for a powerful decision-making tool, referred to as a Partially Observable Markov Decision Process (POMDP). Analysis of this POMDP indicates when the system results in optimal management outcomes.

Controlling SARS-CoV-2 in schools using repetitive testing strategies.

SARS-CoV-2 remains a worldwide emergency. While vaccines have been approved and are widely administered, there is an ongoing debate whether children should be vaccinated or prioritized for vaccination. Therefore, in order to mitigate the spread of more transmissible SARS-CoV-2 variants among children, the use of non-pharmaceutical interventions is still warranted. We investigate the impact of different testing strategies on the SARS-CoV-2 infection dynamics in a primary school environment, using an individual-based modelling approach. Specifically, we consider three testing strategies: (1) symptomatic isolation, where we test symptomatic individuals and isolate them when they test positive, (2) reactive screening, where a class is screened once one symptomatic individual was identified, and (3) repetitive screening, where the school in its entirety is screened on regular time intervals. Through this analysis, we demonstrate that repetitive testing strategies can significantly reduce the attack rate in schools, contrary to a reactive screening or a symptomatic isolation approach. However, when a repetitive testing strategy is in place, more cases will be detected and class and school closures are more easily triggered, leading to a higher number of school days lost per child. While maintaining the epidemic under control with a repetitive testing strategy, we show that absenteeism can be reduced by relaxing class and school closure thresholds.

Impacts of atmospheric stilling and climate warming on cyanobacterial blooms: An individual-based modelling approach

Harmful algal blooms of the freshwater cyanobacteria genus Microcystis are a global problem and are expected to intensify with climate change. In studies of climate change impacts on Microcystis blooms, atmospheric stilling has not been considered. Stilling is expected to occur in some regions of the world with climate warming, and it will affect lake stratification regimes. We tested if stilling could affect water column Microcystis distributions using a novel individual-based model (IBM). Using the IBM coupled to a three-dimensional hydrodynamic model, we assessed responses of colonial Microcystis biomass to wind speed decrease and air temperature increase projected under a future climate. The IBM altered Microcystis colony size using relationships with turbulence from the literature, and included light, temperature, and nutrient effects on Microcystis growth using input data from a shallow urban lake. The model results show that dynamic variations in colony size are critical for accurate prediction of cyanobacterial bloom development and decay. Colony size (mean and variability) increased more than six-fold for a 20% decrease in wind speed compared with a 2 °C increase in air temperature. Our results suggest that atmospheric stilling needs to be included in projections of changes in the frequency, distribution and magnitude of blooms of buoyant, colony-forming cyanobacteria under climate change.

Predicting farmed rainbow trout weight distribution to improve feeding practice: an individual-based model approach.

In the near future, the role of aquaculture in human diet is likely to increase due to the rising demand for fish proteins. With a long tradition of rainbow trout (Oncorhynchus mykiss) farming, Italy is one of the main producers of this species in the European Union (EU). The EU is allocating economic resources to foster the sustainable development of the aquaculture sector, aiming to produce more while using less resources. Precision fish farming (PFF) is a promising approach to achieve this goal and its implementation is being facilitated thanks to the reduction of costs of sensors. PFF will likely lead to a new generation of mathematical dynamic models based on sets of control variables and external forcing functions, coping with Big Data and machine learning techniques. In this work, we developed an individual-based dynamic model for the simulation of the fish size distribution and total biomass of a population of rainbow trout within a raceway. At its core, there is a bioenergetic individual model that can simulate weight changes taking into account water temperature and feeding regime. This model was tested against weight observations collected by a non–invasive monitoring system, that was deployed for the first time in a trout farm. The model allows one to estimate the optimal feeding ration based on fish weight and water temperature. The results indicate that current methodologies, based on the estimation of the average weight, lead to slightly overestimate the feed ration: therefore, the model proposed here would allow one to save feed, thus reducing operational costs.

Reproduction dynamics of planktonic microbial eukaryotes in the open ocean.

Understanding the biology of reproduction of an organismal lineage is important for retracing key evolutionary processes, yet gaining detailed insights often poses major challenges. Planktonic Foraminifera are globally distributed marine microbial eukaryotes and important contributors to the global carbon cycle. They cannot routinely be cultured under laboratory conditions across generations, and thus details of their life cycle remain incomplete. The production of flagellated gametes has long been taken as an indication of exclusively sexual reproduction, but recent research suggests the existence of an additional asexual generation in the life cycle. To gain a better understanding of the reproductive biology of planktonic Foraminifera, we applied a dynamic, individual-based modelling approach with parameters based on laboratory and field observations to test if sexual reproduction is sufficient for maintaining viable populations. We show that temporal synchronization and potentially spatial concentration of gamete release seems inevitable for maintenance of the population under sexual reproduction. We hypothesize that sexual reproduction is likely beneficial during the adaptation to new environments, while population sustenance in stable environments can be ensured through asexual reproduction.

Simulating Microbial Functional Diversity Dynamics in Agricultural Soils: An Individual Based Modeling Approach

New agricultural soil model approaches based on the microbiome dynamics must be considered as they can contribute to understand microbiological soil processes directly linked to substrate metabolism and the influence of these processes on plant growth. The present work presents an approach to the modelling of the interactions of the soil microbial functional diversity with the plant in terms of functions associated to specific processes of organic Carbon and Nitrogen metabolism. The substrates transformations arising in the organic matter that enters as a part of an agricultural scheme are the base for define this metabolism. As result, it has been possible to simulate a rhizospheric soil based on the concept of complex system dynamics and Individual Based Modeling known too as Agent-Based Modeling in an agricultural management context. The explicit definition of the microbiome functional diversity and the processing of the structural elements Carbon and Nitrogen, allowed representing the functional dynamics of this complex system composed by microorganisms, Carbon, Nitrogen and the plant. The variables that reflect the biology and the adaptation to the rhizospheric environment characterized the microorganisms and the assemblage community patterns in time. The main simulations output are system glucose and nitrate levels and an approach to plant growth, all resultant from the metabolic process of the considered Carbon and Nitrogen consortia. The results indicate that the microorganism’s diversity assemblages and its functional expression have a fundamental role in terms of plant growth.

Effects of rodents' behaviours on leptospirosis spread: an individual-based modeling approach

Leptospirosis is a zoonotic disease common in tropical and subtropical regions. This infectious disease is endemic in Sarawak, Malaysia. In recent years, the threat of leptospirosis is on an increasing trend in Sarawak since 2010. The traditional compartmental models assume that the population is homogeneous and shares the same characteristics and behaviours. However, each individual in the population has different and unique behaviour in the real world. Thus, this paper aims to model the leptospirosis spread by adopting the individual-based modeling approach to address the heterogeneity that affects the transmission of the disease. Rodents' behaviours such as active period duration and movement range are incorporated into the model. From the sensitivity analyses, the results show that the movement range of the rodents has a significant impact on the spread of the disease compared to the active period duration. The comparison between simulation results and the actual prevalence data in Sarawak is performed to validate the model. Through regression analysis, the correlations of determination for three outbreaks in Sarawak for the year 2017 are more than 90%. In addition, the normal probability plots for three outbreaks indicate the points follow the line well and are normally distributed. This shows that the proposed individual-based model can predict leptospirosis transmission.