Cash transfer during the COVID-19 pandemic:a multicentre,randomised controlled trial

Objective To evaluate the effect of a one-time cash transfer of$C1000 in people who are unable to physically distance due to insufficient income.Design Open-label,multi-centre,randomised superiority trial.Setting Seven primary care sites in Ontario,Canada;six urban sites associated with St.Michael’s Hospital in Toronto and one in Manitoulin Island.Participants 392 individuals who reported trouble affording basic necessities due to disruptions related to COVID-19.Intervention After random allocation,participants either received the cash transfer of$C1000(n=196)or physical distancing guidelines alone(n=196).Main outcome measures The primary outcome was the maximum number of symptoms consistent with COVID-19 over 14 days.Secondary outcomes were meeting clinical criteria for COVID-19,SARS-CoV-2 presence,number of close contacts,general health and ability to afford basic necessities.Results The primary outcome of number of symptoms reported by participants did not differ between groups after 2 weeks(cash transfer,mean 1.6 vs 1.9,ratio of means 0.83;95%CI 0.56 to 1.24).There were no statistically significant effects on secondary outcomes of the meeting COVID-19 clinical criteria(7.9%vs 12.8%;risk difference−0.05;95%CI−0.11 to 0.01),SARS-CoV-2 presence(0.5%vs 0.6%;risk difference 0.0095%CI−0.02 to 0.02),mean number of close contacts(3.5 vs 3.7;rate ratio 1.10;95%CI 0.83 to 1.46),general health very good or excellent(60%vs 63%;risk difference−0.0395%CI−0.14 to 0.08)and ability to make ends meet(52%vs 51%;risk difference 0.0195%CI−0.10 to 0.12).Conclusions A single cash transfer did not reduce the COVID-19 symptoms or improve the ability to afford necessities.Further studies are needed to determine whether some groups may benefit from financial supports and to determine if a higher level of support is beneficial.Trial registration number NCT04359264.

Estimating effective reproduction number using generation time versus serial interval,with application to COVID-19 in the Greater Toronto Area,Canada

BACKGROUND.The effective reproduction number Re(t)is a critical measure of epidemic potential.Re(t)can be calculated in near real time using an incidence time series and the generation time distribution:the time between infection events in an infector-infectee pair.In calculating Re(t),the generation time distribution is often approximated by the serial interval distribution:the time between symptom onset in an infector-infectee pair.However,while generation time must be positive by definition,serial interval can be negative if transmission can occur before symptoms,such as in COVID-19,rendering such an approximation improper in some contexts.METHODS.We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions.We then compared estimates of Re(t)for COVID-19 in the Greater Toronto Area of Canada using:negative-permitting versus non-negative serial interval distributions,versus the inferred generation time distribution.RESULTS.We estimated the generation time of COVID-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days.Relative to the generation time distribution,non-negative serial interval distribution caused overestimation of Re(t)due to larger mean,while negative-permitting serial interval distribution caused underestimation of Re(t)due to larger variance.IMPLICATIONS.Approximation of the generation time distribution of COVID-19 with non-negative or negative-permitting serial interval distributions when calculating Re(t)may result in over or underestimation of transmission potential,respectively.

Contribution of high risk groups’unmet needs may be underestimated in epidemic models without risk turnover:A mechanistic modelling analysis

Background:Epidemic models of sexually transmitted infections(STIs)are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction(tPAF)of unmet prevention and treatment needs within risk groups.However,evidence suggests that STI risk is dynamic over an individual’s sexual life course,which manifests as turnover between risk groups.We sought to examine the mechanisms by which turnover influences modelled projections of the tPAF of high risk groups.Methods:We developed a unifying,data-guided framework to simulate risk group turnover in deterministic,compartmental transmission models.We applied the framework to an illustrative model of an STI and examined the mechanisms by which risk group turnover influenced equilibrium prevalence across risk groups.We then fit a model with and without turnover to the same risk-stratified STI prevalence targets and compared the inferred level of risk heterogeneity and tPAF of the highest risk group projected by the two models.Results:The influence of turnover on group-specific prevalence was mediated by three main phenomena:movement of previously high risk individuals with the infection into lower risk groups;changes to herd effect in the highest risk group;and changes in the number of partnerships where transmission can occur.Faster turnover led to a smaller ratio of STI prevalence between the highest and lowest risk groups.Compared to the fitted model without turnover,the fitted model with turnover inferred greater risk heterogeneity and consistently projected a larger tPAF of the highest risk group over time.Implications:If turnover is not captured in epidemic models,the projected contribution of high risk groups,and thus,the potential impact of prioritizing interventions to address their needs,could be underestimated.To aid the next generation of tPAF models,data collection efforts to parameterize risk group turnover should be prioritized.

Corrigendum to“Estimating effective reproduction number using generation time versus serial interval,with application to covid-19 in the Greater Toronto Area,Canada”[Infectious Disease Modelling 5(2020)889e896]

In the originally published version of this work,the parametersθ^(*)=[a,b]were calculated incorrectly because the Kullback-Leibler divergence was defined in the wrong direction in our code.The impact on generation time parameters and statistics is as follows(original/fixed):shape(a):1.813/1.633,scale(b):2.199/2.498,mean:3.99/4.08,SD:2.96/3.19.The qualitative interpretation of results is unchanged.We sincerely apologize for this error.The error correction is shown here:https://github.com/mishra-lab/covid-r/commit/aaef512 and a corrected draft manuscript is shown here.