Implementation and applications of EMOD, an individual-based multi-disease modeling platform

ABSTRACT Individual-based models provide modularity and structural flexibility necessary for modeling of infectious diseases at the within-host and population levels, but are challenging to implement. Levels of complexity can exceed the capacity and timescales for students and trainees in most academic institutions. Here we describe the process and advantages of a multi-disease framework approach developed with formal software support. The epidemiological modeling software, EMOD, has undergone a decade of software development. It is structured so that a majority of code is shared across disease modeling including malaria, HIV, tuberculosis, dengue, polio and typhoid. In additional to implementation efficiency, the sharing increases code usage and testing. The freely available codebase also includes hundreds of regression tests, scientific feature tests and component tests to help verify functionality and avoid inadvertent changes to functionality during future development. Here we describe the levels of detail, flexible configurability and modularity enabled by EMOD and the role of software development principles and processes in its development.

[1]  P. Eckhoff,et al.  P. falciparum Infection Durations and Infectiousness Are Shaped by Antigenic Variation and Innate and Adaptive Host Immunity in a Mathematical Model , 2012, PloS one.

[2]  Philip A. Eckhoff,et al.  Targeting HIV services to male migrant workers in southern Africa would not reverse generalized HIV epidemics in their home communities: a mathematical modeling analysis , 2015, International health.

[3]  Philip A. Eckhoff,et al.  The EMOD Individual‐Based Model , 2016 .

[4]  M. Keeling,et al.  Modeling Infectious Diseases in Humans and Animals , 2007 .

[5]  S. Levin,et al.  Mathematical and Computational Challenges in Population Biology and Ecosystems Science , 1997, Science.

[6]  P. Eckhoff,et al.  Malaria parasite diversity and transmission intensity affect development of parasitological immunity in a mathematical model , 2012, Malaria Journal.

[7]  R. May,et al.  Population biology of infectious diseases: Part II , 1979, Nature.

[8]  Patrick S Olsen,et al.  Improving retention in HIV care among adolescents and adults in low- and middle-income countries: A systematic review of the literature , 2017, PloS one.

[9]  Matthias Egger,et al.  Retention and mortality on antiretroviral therapy in sub‐Saharan Africa: collaborative analyses of HIV treatment programmes , 2018, Journal of the International AIDS Society.

[10]  Till Bärnighausen,et al.  Health benefits, costs, and cost-effectiveness of earlier eligibility for adult antiretroviral therapy and expanded treatment coverage: a combined analysis of 12 mathematical models. , 2014, The Lancet. Global health.

[11]  Carolyn Bolton Moore,et al.  Estimated mortality on HIV treatment among active patients and patients lost to follow-up in 4 provinces of Zambia: Findings from a multistage sampling-based survey , 2018, PLoS medicine.

[12]  Christophe Fraser,et al.  Assessment of epidemic projections using recent HIV survey data in South Africa: a validation analysis of ten mathematical models of HIV epidemiology in the antiretroviral therapy era. , 2015, The Lancet. Global health.

[13]  Till Bärnighausen,et al.  Treatment eligibility and retention in clinical HIV care: A regression discontinuity study in South Africa , 2017, PLoS medicine.

[14]  Michael Famulare,et al.  The risk of type 2 oral polio vaccine use in post-cessation outbreak response , 2017, BMC Medicine.

[15]  Kara Wools-Kaloustian,et al.  The Causal Effect of Tracing by Peer Health Workers on Return to Clinic Among Patients Who Were Lost to Follow-up From Antiretroviral Therapy in Eastern Africa: A “Natural Experiment” Arising From Surveillance of Lost Patients , 2017, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[16]  Jaline Gerardin,et al.  Characterization of the infectious reservoir of malaria with an agent-based model calibrated to age-stratified parasite densities and infectiousness , 2015, Malaria Journal.

[17]  Karima R. Nigmatulina,et al.  An Environmental Data Set for Vector-Borne Disease Modeling and Epidemiology , 2014, PloS one.

[18]  Busiku Hamainza,et al.  Effectiveness of reactive case detection for malaria elimination in three archetypical transmission settings: a modelling study , 2017, Malaria Journal.

[19]  Austin Burt,et al.  Impact of mosquito gene drive on malaria elimination in a computational model with explicit spatial and temporal dynamics , 2016, Proceedings of the National Academy of Sciences.

[20]  Philip A. Eckhoff,et al.  Age-targeted HIV treatment and primary prevention as a ‘ring fence’ to efficiently interrupt the age patterns of transmission in generalized epidemic settings in South Africa , 2016, International health.

[21]  Niel Hens,et al.  Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015) , 2017, BMC Infectious Diseases.

[22]  Jeffrey W. Eaton,et al.  HIV Treatment as Prevention: Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa , 2012, PLoS medicine.

[23]  Leigh Tesfatsion,et al.  Agent-Based Computational Economics: Growing Economies From the Bottom Up , 2002, Artificial Life.

[24]  Guillaume Chabot-Couture,et al.  A spatial model of Wild Poliovirus Type 1 in Kano State, Nigeria: calibration and assessment of elimination probability , 2016, BMC Infectious Diseases.

[25]  Anna Bershteyn,et al.  Association between economic growth and early childhood nutrition. , 2015, The Lancet. Global health.

[26]  Nena do Nascimento,et al.  Where is the evidence? The use of routinely-collected patient data to retain adults on antiretroviral treatment in low and middle income countries–a state of the evidence review , 2018, AIDS care.

[27]  O P Judson,et al.  The rise of the individual-based model in ecology. , 1994, Trends in ecology & evolution.

[28]  Steven F. Railsback,et al.  Agent-Based and Individual-Based Modeling: A Practical Introduction , 2011 .

[29]  P. Eckhoff A malaria transmission-directed model of mosquito life cycle and ecology , 2011, Malaria Journal.

[30]  Neff Walker,et al.  Mathematical models in the evaluation of health programmes , 2011, The Lancet.

[31]  G. Huse Individual‐based Modeling and Ecology , 2008 .