Table 2.

Summary of gaps in existing inference techniques and suggestions for future research.

	Presently available data and methods	Suggestions for future research
Genomic	•Pathogen genomic sequences are largely consensus in nature	•Use deep-sequencing for within-host identification of minor variants
Spatial	•Individual to individual •Farm to farm •Country to country	•Use community-level resolution such as household to household, zipcode to zipcode, or neighborhood-based geographical locations, which are reasonable for targeting of public-health interventions
Methods	•Fitted to disease •Small sample size •Biased towards the most severe cases	•Perform power analysis to identify sample size for inference •Reduce selection bias in data by generating it from the community which captures a wide range of infections •Incorporate supplementary information such as social networking, point-of-care data, and electronic medical record (EMR) data. Social networking data can capture social and family contact structures which can augment information about how transmission spreads. Point-of-care data can be utilized where access to clinics is not available or feasible. EMR data includes information such as family, social, and medication history
Data Generation	•Clinical	•Community-generated data from the wide range of cases in the community who do not necessarily report to a clinic or are symptomatic •Crowdsourced data which includes multitudes of factors such as social network structures and mobility data •Participatory self-reported data
Parameters	•Transmission tree •Rate of infectiousness •Rate of recovery •Proportion of infected hosts sampled •Genetic outliers •Superspreaders	•Community parameters capturing location or neighborhood-based infectiousness and transmissibility essential for proactive intervention such as quarantine and vaccination •Incorporate population stochastics such as mobility and transportation •Foreign exports