scholarly journals Edge-based compartmental modelling for infectious disease spread

2011 ◽  
Vol 9 (70) ◽  
pp. 890-906 ◽  
Author(s):  
Joel C. Miller ◽  
Anja C. Slim ◽  
Erik M. Volz
Keyword(s):  
Infectious Disease ◽  
Mass Action ◽  
Disease Spread ◽  
Contact Rate ◽  
Compartmental Modelling ◽  
Contact Rates ◽  
Differential Equation Models ◽  
Social Heterogeneity ◽  
Edge Based ◽  
The Impact

The primary tool for predicting infectious disease spread and intervention effectiveness is the mass action susceptible–infected–recovered model of Kermack & McKendrick. Its usefulness derives largely from its conceptual and mathematical simplicity; however, it incorrectly assumes that all individuals have the same contact rate and partnerships are fleeting. In this study, we introduce edge-based compartmental modelling , a technique eliminating these assumptions. We derive simple ordinary differential equation models capturing social heterogeneity (heterogeneous contact rates) while explicitly considering the impact of partnership duration. We introduce a graphical interpretation allowing for easy derivation and communication of the model and focus on applying the technique under different assumptions about how contact rates are distributed and how long partnerships last.

scholarly journals Contact surveys reveal heterogeneities in age-group contributions to SARS-CoV-2 dynamics in the United States

2021 ◽  
Author(s):  
Taylor Chin ◽  
Dennis M. Feehan ◽  
Caroline O. Buckee ◽  
Ayesha S. Mahmud
Keyword(s):  
United States ◽  
Age Groups ◽  
The United States ◽  
Contact Rate ◽  
Age Group ◽  
Contact Rates ◽  
Contact Patterns ◽  
Age Structured ◽  
The Impact ◽  
Over Time

SARS-CoV-2 is spread primarily through person-to-person contacts. Quantifying population contact rates is important for understanding the impact of physical distancing policies and for modeling COVID-19, but contact patterns have changed substantially over time due to shifting policies and behaviors. There are surprisingly few empirical estimates of age-structured contact rates in the United States both before and throughout the COVID-19 pandemic that capture these changes. Here, we use data from six waves of the Berkeley Interpersonal Contact Survey (BICS), which collected detailed contact data between March 22, 2020 and February 15, 2021 across six metropolitan designated market areas (DMA) in the United States. Contact rates were low across all six DMAs at the start of the pandemic. We find steady increases in the mean and median number of contacts across these localities over time, as well as a greater proportion of respondents reporting a high number of contacts. We also find that young adults between ages 18 and 34 reported more contacts on average compared to other age groups. The 65 and older age group consistently reported low levels of contact throughout the study period. To understand the impact of these changing contact patterns, we simulate COVID-19 dynamics in each DMA using an age-structured mechanistic model. We compare results from models that use BICS contact rate estimates versus commonly used alternative contact rate sources. We find that simulations parameterized with BICS estimates give insight into time-varying changes in relative incidence by age group that are not captured in the absence of these frequently updated estimates. We also find that simulation results based on BICS estimates closely match observed data on the age distribution of cases, and changes in these distributions over time. Together these findings highlight the role of different age groups in driving and sustaining SARS-CoV-2 transmission in the U.S. We also show the utility of repeated contact surveys in revealing heterogeneities in the epidemiology of COVID-19 across localities in the United States.

scholarly journals Adaptive social contact rates induce complex dynamics during epidemics

2020 ◽  
Author(s):  
Ronan F. Arthur ◽  
James H. Jones ◽  
Matthew H. Bonds ◽  
Yoav Ram ◽  
Marcus W. Feldman
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Social Contact ◽  
Contact Rate ◽  
Complex Dynamic ◽  
The Public ◽  
Oscillatory Dynamics ◽  
Delayed Information ◽  
Contact Rates ◽  
Epidemic Response

AbstractThe COVID-19 pandemic has posed a significant dilemma for governments across the globe. The public health consequences of inaction are catastrophic; but the economic consequences of drastic action are likewise catastrophic. Governments must therefore strike a balance in the face of these trade-offs. But with critical uncertainty about how to find such a balance, they are forced to experiment with their interventions and await the results of their experimentation. Models have proved inaccurate because behavioral response patterns are either not factored in or are hard to predict. One crucial behavioral response in a pandemic is adaptive social contact: potentially infectious contact between people is deliberately reduced either individually or by fiat; and this must be balanced against the economic cost of having fewer people in contact and therefore active in the labor force. We develop a model for adaptive optimal control of the effective social contact rate within a Susceptible-Infectious-Susceptible (SIS) epidemic model using a dynamic utility function with delayed information. This utility function trades off the population-wide contact rate with the expected cost and risk of increasing infections. Our analytical and computational analysis of this simple discrete-time deterministic model reveals the existence of a non-zero equilibrium, oscillatory dynamics around this equilibrium under some parametric conditions, and complex dynamic regimes that shift under small parameter perturbations. These results support the supposition that infectious disease dynamics under adaptive behavior-change may have an indifference point, may produce oscillatory dynamics without other forcing, and constitute complex adaptive systems with associated dynamics. Implications for COVID-19 include an expectation of fluctuations, for a considerable time, around a quasi-equilibrium that balances public health and economic priorities, that shows multiple peaks and surges in some scenarios, and that implies a high degree of uncertainty in mathematical projections.Author summaryEpidemic response in the form of social contact reduction, such as has been utilized during the ongoing COVID-19 pandemic, presents inherent tradeoffs between the economic costs of reducing social contacts and the public health costs of neglecting to do so. Such tradeoffs introduce an interactive, iterative mechanism which adds complexity to an infectious disease system. Consequently, infectious disease modeling typically has not included dynamic behavior change that must address such a tradeoff. Here, we develop a theoretical model that introduces lost or gained economic and public health utility through the adjustment of social contact rates with delayed information. We find this model produces an equilibrium, a point of indifference where the tradeoff is neutral, and at which a disease will be endemic for a long period of time. Under small perturbations, this model exhibits complex dynamic regimes, including oscillatory behavior, runaway exponential growth, and eradication. These dynamics suggest that for epidemic response that relies on social contact reduction, secondary waves and surges with accompanied business re-closures and shutdowns may be expected, and that accurate projection under such circumstances is unlikely.

Paleoepidemiological Considerations of Mobility and Population Interaction in the Spread of Infectious Diseases in the Prehistoric Past

2021 ◽  
Author(s):  
Melandri Vlok ◽  
Hallie Buckley
Keyword(s):  
Infectious Disease ◽  
Population Density ◽  
Infectious Diseases ◽  
Residential Mobility ◽  
Human Interaction ◽  
Disease Spread ◽  
Relative Risk Ratio ◽  
Disease Patterns ◽  
Population Interaction ◽  
The Impact

The processes of human mobility have been well demonstrated to influence the spread of infectious disease globally in the present and the past. However, to date, paleoepidemiological research has focused more on factors of residential mobility and population density as drivers for epidemiological shifts in prehistoric infectious disease patterns. A strong body of epidemiological literature exists for the dynamics of infectious disease spread through networks of mobility and interaction. We review the epidemiological theory of infectious disease spread and propose frameworks for application of this theory to bioarchaeology. We outline problems with current definitions of prehistoric mobility and propose a framework shift with focus on population interactions as nodes for disease transmission. To conceptualize this new framework, we produced a theoretical model that considers the interplay between climate suitability, population density, residential mobility, and human interaction levels to influence infectious disease patterns in prehistoric assemblages. We then tested observable effects of this model in paleoepidemiological data from Asia (n = 343). Relative risk ratio analysis and correlations were used to test the impact of population interaction, residential mobility, population density, climate, and subsistence on the prevalence and diversity of infectious diseases. Our statistical results showed higher levels of population interaction led to significantly higher prevalence of infectious disease in sedentary populations and a significant increase in pathogen diversity in mobile populations. We recommend that population interaction be included as an important component of infectious disease analysis of prehistoric population health alongside other biosocial factors, such as sedentism and population density.   Daar is goed gedemonstreer dat die prosesse van menslike mobiliteit die verspreiding van aansteeklike siektes wêreldwyd in die hede en in die verlede beïnvloed. Maar tot op hede het paleo-epidemiologiese navorsing egter meer gefokus op faktore van residensiële mobiliteit en bevolkingsdigtheid as dryfvere vir epidemiologiese verskuiwings in die prehistoriese infeksiesiektepatrone. Sterk epidemiologiese literatuur bestaan vir die dinamika van aansteeklike siektes wat versprei word deur netwerke van mobiliteit en interaksie. Ons ondersoek die epidemiologiese teorie van die verspreiding van aansteeklike siektes en stel raamwerke voor vir die toepassing van hierdie teorie op die bioargeologie. Ons skets probleme met huidige definisies van prehistoriese mobiliteit en stel ‘n raamwerk verskuiwing voor met die fokus op bevolkings-interaksies as nodusse vir oordrag van siektes. Om hierdie nuwe raamwerk te konseptualiseer, het ons ‘n teoretiese model vervaardig wat die wisselwerking tussen klimaatsgeskiktheid, bevolkingsdigtheid, residensiële mobiliteit en menslike interaksievlakke oorweeg om die infeksiesiektepatrone in prehistoriese samestellings te beïnvloed. Daarna het ons die waarneembare effekte van hierdie model getoets in paleo-epidemiologiese data uit Asië (n = 343). Relatiewe risiko-verhoudingsanalise en korrelasies is gebruik om die impak van bevolkings-interaksie, residensiële mobiliteit, bevolkingsdigtheid, klimaat en bestaan op die voorkoms en diversiteit van aansteeklike siektes te toets. Ons statistiese resultate het gedemonstreer dat hoër vlakke van bevolkings-interaksie gelei het tot aansienlik hoër voorkoms van aansteeklike siektes in sittende bevolkings en ‘n beduidende toename in patogeen diversiteit in mobiele bevolkings. Ons beveel aan dat bevolkings-interaksie ingesluit word as ‘n belangrike komponent van die aantstekingsiekte-ontleding van die prehistoriese bevolkingsgesondheid, tesame met ander biososiale faktore soos sedentisme en bevolkingsdigtheid.

scholarly journals Threshold analyses on rates of testing, transmission, and contact for COVID-19 control in a university setting

2020 ◽  
Author(s):  
Xinmeng Zhao ◽  
Hanisha Anand Tatapudi ◽  
George Corey ◽  
Chaitra Gopalappa
Keyword(s):  
Transmission Rate ◽  
Full Range ◽  
Face Mask ◽  
Contact Rate ◽  
Contact Rates ◽  
Time Of Infection ◽  
Four Levels ◽  
The Impact ◽  
Mass Test ◽  
Rate Threshold

We simulated epidemic projections of a potential COVID-19 outbreak in a university population of 38,000 persons, under varying combinations of mass test rate (0% to 10%), contact trace and test rate (0% to 50%), transmission rate (probability of transmission per contact per day), and contact rate (number of contacts per person per day). We simulated four levels of transmission rate, 14% (average baseline), 8% (average for face mask use), 5.4% (average for 3ft distancing), and 2.5% (average for 6ft distancing and face mask use), interpolating results to the full range to understand the impact of uncertainty in effectiveness, feasibility, and adherence of face mask use and physical distancing. We evaluated contact rates between 1 and 25, to identify the threshold that, if exceeded, could lead to several deaths. When transmission rate was 8%, for trace and test at 50%, the contact rate threshold was 8. However, any time delays in trace, test, and isolation quickly raised the number of deaths. Keeping contact rate to 3 or below was more robust to testing delays, keeping deaths below 1 up to a delay of 5 days from the time of infection to diagnosis and isolation. For a contact rate of 3, the number of trace and tests peaked to about 70 per day and relaxed to 25 with the addition of 10% mass test. When transmission rate was 5.4%, for trace and test at 50%, the contact rate threshold was 10. However, keeping contact rate to 4 or below was more robust to delays in testing, keeping deaths below 1 up to a delay of 6 days from the time of infection to diagnosis and isolation. For contact rate of 4, the number of trace and tests peaked at 50 per day and relaxed to 10 per day with the addition of 10% mass test. Threshold estimates can help develop on-campus scheduling and indoor-spacing plans in conjunction with plans for asymptomatic testing for COVID-19. Combination thresholds should be selected specific to the setting based on an assessment of the feasibility and resource 48 availability for testing and quarantine.

Paleoepidemiological Considerations of Mobility and Population Interaction in the Spread of Infectious Diseases in the Prehistoric Past

2021 ◽  
Author(s):  
Melandri Vlok ◽  
Hallie Buckley
Keyword(s):  
Infectious Disease ◽  
Population Density ◽  
Infectious Diseases ◽  
Residential Mobility ◽  
Human Interaction ◽  
Disease Spread ◽  
Relative Risk Ratio ◽  
Disease Patterns ◽  
Population Interaction ◽  
The Impact

The processes of human mobility have been well demonstrated to influence the spread of infectious disease globally in the present and the past. However, to date, paleoepidemiological research has focused more on factors of residential mobility and population density as drivers for epidemiological shifts in prehistoric infectious disease patterns. A strong body of epidemiological literature exists for the dynamics of infectious disease spread through networks of mobility and interaction. We review the epidemiological theory of infectious disease spread and propose frameworks for application of this theory to bioarchaeology. We outline problems with current definitions of prehistoric mobility and propose a framework shift with focus on population interactions as nodes for disease transmission. To conceptualize this new framework, we produced a theoretical model that considers the interplay between climate suitability, population density, residential mobility, and human interaction levels to influence infectious disease patterns in prehistoric assemblages. We then tested observable effects of this model in paleoepidemiological data from Asia (n = 343). Relative risk ratio analysis and correlations were used to test the impact of population interaction, residential mobility, population density, climate, and subsistence on the prevalence and diversity of infectious diseases. Our statistical results showed higher levels of population interaction led to significantly higher prevalence of infectious disease in sedentary populations and a significant increase in pathogen diversity in mobile populations. We recommend that population interaction be included as an important component of infectious disease analysis of prehistoric population health alongside other biosocial factors, such as sedentism and population density.   Daar is goed gedemonstreer dat die prosesse van menslike mobiliteit die verspreiding van aansteeklike siektes wêreldwyd in die hede en in die verlede beïnvloed. Maar tot op hede het paleo-epidemiologiese navorsing egter meer gefokus op faktore van residensiële mobiliteit en bevolkingsdigtheid as dryfvere vir epidemiologiese verskuiwings in die prehistoriese infeksiesiektepatrone. Sterk epidemiologiese literatuur bestaan vir die dinamika van aansteeklike siektes wat versprei word deur netwerke van mobiliteit en interaksie. Ons ondersoek die epidemiologiese teorie van die verspreiding van aansteeklike siektes en stel raamwerke voor vir die toepassing van hierdie teorie op die bio-argeologie. Ons skets probleme met huidige definisies van prehistoriese mobiliteit en stel ‘n raamwerk verskuiwing voor met die fokus op bevolkings-interaksies as nodusse vir oordrag van siektes. Om hierdie nuwe raamwerk te konseptualiseer, het ons ‘n teoretiese model vervaardig wat die wisselwerking tussen klimaatsgeskiktheid, bevolkingsdigtheid, residensiële mobiliteit en menslike interaksievlakke oorweeg om die infeksiesiektepatrone in prehistoriese samestellings te beïnvloed. Daarna het ons die waarneembare effekte van hierdie model getoets in paleo-epidemiologiese data uit Asië (n = 343). Relatiewe risiko-verhoudingsanalise en korrelasies is gebruik om die impak van bevolkings-interaksie, residensiële mobiliteit, bevolkingsdigtheid, klimaat en bestaan op die voorkoms en diversiteit van aansteeklike siektes te toets. Ons statistiese resultate het gedemonstreer dat hoër vlakke van bevolkings-interaksie gelei het tot aansienlik hoër voorkoms van aansteeklike siektes in sittende bevolkings en ‘n beduidende toename in patogeen diversiteit in mobiele bevolkings. Ons beveel aan dat bevolkings-interaksie ingesluit word as ‘n belangrike komponent van die aantstekingsiekte-ontleding van die prehistoriese bevolkingsgesondheid, tesame met ander biososiale faktore soos sedentisme en bevolkingsdigtheid.

scholarly journals The Influence of War and Conflict on Infectious Disease: A Rapid Review of Historical Lessons We Have Yet to Learn

2021 ◽  
Vol 13 (19) ◽  
pp. 10783
Author(s):  
Krzysztof Goniewicz ◽  
Frederick M. Burkle ◽  
Simon Horne ◽  
Marta Borowska-Stefańska ◽  
Szymon Wiśniewski ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Low Income ◽  
Respiratory Infections ◽  
Communicable Disease ◽  
Armed Conflicts ◽  
Disease Spread ◽  
Low Income Countries ◽  
Rapid Review ◽  
The Impact

Armed conflicts degrade established healthcare systems, which typically manifests as a resurgence of preventable infectious diseases. While 70% of deaths globally are now from non-communicable disease; in low-income countries, respiratory infections, diarrheal illness, malaria, tuberculosis, and HIV/AIDs are all in the top 10 causes of death. The burden of these infectious diseases is exacerbated by armed conflict, translating into even more dramatic long-term consequences. This rapid evidence review searched electronic databases in PubMed, Scopus, and Web of Science. Of 381 identified publications, 73 were included in this review. Several authors indicate that the impact of infectious diseases increases in wars and armed conflicts due to disruption to surveillance and response systems that were often poorly developed to begin with. Although the true impact of conflict on infectious disease spread is not known and requires further research, the link between them is indisputable. Current decision-making management systems are insufficient and only pass the baton to the next unwary generation.

scholarly journals Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng-Chun Chang ◽  
Rebecca Kahn ◽  
Yu-An Li ◽  
Cheng-Sheng Lee ◽  
Caroline O. Buckee ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Initial Conditions ◽  
Human Mobility ◽  
Human Movement ◽  
Disease Spread ◽  
Spatial Spread ◽  
Movement Data ◽  
Intercity Travel ◽  
Travel Restrictions ◽  
The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

scholarly journals Parasitism of Aedes albopictus by Ascogregarina taiwanensis lowers its competitive ability against Aedes triseriatus

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Emma Stump ◽  
Lauren M. Childs ◽  
Melody Walker
Keyword(s):  
Population Dynamics ◽  
Competitive Advantage ◽  
Aedes Albopictus ◽  
Abiotic Factors ◽  
Mosquito Species ◽  
Disease Spread ◽  
Aedes Triseriatus ◽  
La Crosse Virus ◽  
The Impact ◽  
Ascogregarina Taiwanensis

Abstract Background Mosquitoes are vectors for diseases such as dengue, malaria and La Crosse virus that significantly impact the human population. When multiple mosquito species are present, the competition between species may alter population dynamics as well as disease spread. Two mosquito species, Aedes albopictus and Aedes triseriatus, both inhabit areas where La Crosse virus is found. Infection of Aedes albopictus by the parasite Ascogregarina taiwanensis and Aedes triseriatus by the parasite Ascogregarina barretti can decrease a mosquito’s fitness, respectively. In particular, the decrease in fitness of Aedes albopictus occurs through the impact of Ascogregarina taiwanensis on female fecundity, larval development rate, and larval mortality and may impact its initial competitive advantage over Aedes triseriatus during invasion. Methods We examine the effects of parasitism of gregarine parasites on Aedes albopictus and triseriatus population dynamics and competition with a focus on when Aedes albopictus is new to an area. We build a compartmental model including competition between Aedes albopictus and triseriatus while under parasitism of the gregarine parasites. Using parameters based on the literature, we simulate the dynamics and analyze the equilibrium population proportion of the two species. We consider the presence of both parasites and potential dilution effects. Results We show that increased levels of parasitism in Aedes albopictus will decrease the initial competitive advantage of the species over Aedes triseriatus and increase the survivorship of Aedes triseriatus. We find Aedes albopictus is better able to invade when there is more extreme parasitism of Aedes triseriatus. Furthermore, although the transient dynamics differ, dilution of the parasite density through uptake by both species does not alter the equilibrium population sizes of either species. Conclusions Mosquito population dynamics are affected by many factors, such as abiotic factors (e.g. temperature and humidity) and competition between mosquito species. This is especially true when multiple mosquito species are vying to live in the same area. Knowledge of how population dynamics are affected by gregarine parasites among competing species can inform future mosquito control efforts and help prevent the spread of vector-borne disease.

scholarly journals Stability analysis of a dynamical model of tuberculosis with incomplete treatment

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ihsan Ullah ◽  
Saeed Ahmad ◽  
Qasem Al-Mdallal ◽  
Zareen A. Khan ◽  
Hasib Khan ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Contact Rate ◽  
Asymptotically Stable ◽  
Treatment Rate ◽  
Globally Asymptotically Stable ◽  
Reproduction Ratio ◽  
Efficient Treatment ◽  
Effective Contact ◽  
The Impact ◽  
Incomplete Treatment

Abstract A simple deterministic epidemic model for tuberculosis is addressed in this article. The impact of effective contact rate, treatment rate, and incomplete treatment versus efficient treatment is investigated. We also analyze the asymptotic behavior, spread, and possible eradication of the TB infection. It is observed that the disease transmission dynamics is characterized by the basic reproduction ratio $\Re _{0}$ ℜ 0 ; if $\Re _{0}<1$ ℜ 0 < 1 , there is only a disease-free equilibrium which is both locally and globally asymptotically stable. Moreover, for $\Re _{0}>1$ ℜ 0 > 1 , a unique positive endemic equilibrium exists which is globally asymptotically stable. The global stability of the equilibria is shown via Lyapunov function. It is also obtained that incomplete treatment of TB causes increase in disease infection while efficient treatment results in a reduction in TB. Finally, for the estimated parameters, some numerical simulations are performed to verify the analytical results. These numerical results indicate that decrease in the effective contact rate λ and increase in the treatment rate γ play a significant role in the TB infection control.

Sign in / Sign up

Export Citation Format

Share Document