# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
## 2 Related Work
### Meeting Distribution-Based Models  
Unlike agent-based and contact tracing-based models that focus on individual-level contact data, meeting distribution-based models rely on population-level interaction patterns. [PERSON] et al. (2018) estimate age-stratified contact matrices--the average number of daily contacts between individuals of different age groups--and calculate reductions in the basic reproduction number using the next-generation method. Their study showed social distancing measures during COVID-19 reduced the basic reproduction number by 25% compared to what it would have been had there been no social distancing.  
[PERSON] et al. (2018) construct age-stratified contact matrices (saying how likely persons of different age groups would meet each other in a variety of locales) using socio-demographic data and POLYMOD surveys. They offer a case study of estimating the reproduction number of the H1N1 2009 epidemic in a variety of countries. They show that taking into account the variability in the distribution of contacts gives greater accuracy compared to a uniform mixing model.  
[PERSON] et al. (2018) develop a theoretical transmission model using empirical gathering size data from the BBC Pandemic and Copenhagen Networks Study (CNS). Their transmission model calculates the expected number of new infections at a certain gathering size based on transmission probability and population proportions of susceptible and infectious individuals. Their study shows that for COVID-19, gatherings would have to be severely restricted in size to prevent epidemic spread.  
[PERSON] et al. (2018) construct an age-specific forecasting framework and apply it to two age-stratified time-series data; the first one is the incidence of SARS-CoV-2 infection estimated from the national infection and antibody prevalence survey, and the second one is the reported cases based on the UK COVID-19 dashboard. They predict new infections by using real-time contact patterns combined with a semi-mechanistic model that estimates how people of different ages interact and transmit the virus, accounting for changes in immunity, vaccination, and infection rates over time. Their study finds that incorporating age interaction can improve infection predictions, particularly in children and older adults, and the social contact data informed model performs the best in the short term (between 2-4 weeks) and the winter months in 2020-2021.  
[PERSON] et al. (2018) present a method to analyze age-specific variations in transmission parameters associated with susceptibility and infectiousness to SARS-CoV-2 infection. They combine social contact data with the next-generation principle to estimate relative infection rates across different age groups. Their findings indicate that children, especially preschool aged young children, have about half the susceptibility of adults. Although this study does not explicitly track individual-level meetings, it estimates contact distributions using diary-based social contact data, which serve as an aggregate representation of meeting patterns across age groups.  
An important insight from the above excellent work is that knowing as much information as possible about meetings is helpful. Practically speaking, however, we find that basically no contact tracing data is available or, if it is, then only in small quantities. What is available is survey data indicating how many people each person meets.