Contact tracing efficiency, transmission heterogeneity, and accelerating COVID-19 epidemics

Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 and other pathogens. However, manual contact tracing is a time-consuming process and as case numbers increase a smaller fraction of cases’ contacts can be traced, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between increasing cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using deterministic and stochastic compartmental models of SARS-CoV-2. We found that Rt increased sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt initially increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections may be more complicated). Contact tracing efficacy decreased sharply with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases. They also highlight the synergistic value of high capacity, easy access testing and rapid turn-around of testing results, and outreach efforts to encourage symptomatic cases to be tested immediately after symptom onset.

Prevalence and seroprevalence of Plasmodium infection in Myanmar reveals highly heterogeneous transmission and a large hidden reservoir of infection

Malaria incidence in Myanmar has significantly reduced over recent years, however, completeness and timeliness of incidence data remain a challenge. The first ever nationwide malaria infection and seroprevalence survey was conducted in Myanmar in 2015 to better understand malaria epidemiology and highlight gaps in Annual Parasite Index (API) data. The survey was a cross-sectional two-stage stratified cluster-randomised household survey conducted from July-October 2015. Blood samples were collected from household members for ultra-sensitive PCR and serology testing for P. falciparum and P. vivax. Data was gathered on demography and a priori risk factors of participants. Data was analysed nationally and within each of four domains defined by API data. Prevalence and seroprevalence of malaria were 0.74% and 16.01% nationwide, respectively. Prevalent infection was primarily asymptomatic P. vivax, while P. falciparum was predominant in serology. There was large heterogeneity between villages and by domain. At the township level, API showed moderate correlation with P. falciparum seroprevalence. Risk factors for infection included socioeconomic status, domain, and household ownership of nets. Three K13 P. falciparum mutants were found in highly prevalent villages. There results highlight high heterogeneity of both P. falciparum and P. vivax transmission between villages, accentuated by a large hidden reservoir of asymptomatic P. vivax infection not captured by incidence data, and representing challenges for malaria elimination. Village-level surveillance and stratification to guide interventions to suit local context and targeting of transmission foci with evidence of drug resistance would aid elimination efforts.

Frequency Selectivity of Neural Circuits with Heterogeneous Discrete Transmission Delays

Neurons are connected to other neurons by axons and dendrites that conduct signals with finite velocities, resulting in delays between the firing of a neuron and the arrival of the resultant impulse at other neurons. Since delays greatly complicate the analytical treatment and interpretation of models, they are usually neglected or taken to be uniform, leading to a lack in the comprehension of the effects of delays in neural systems. This letter shows that heterogeneous transmission delays make small groups of neurons respond selectively to inputs with differing frequency spectra. By studying a single integrate-and-fire neuron receiving correlated time-shifted inputs, it is shown how the frequency response is linked to both the strengths and delay times of the afferent connections. The results show that incorporating delays alters the functioning of neural networks, and changes the effect that neural connections and synaptic strengths have.

Role of Heterogeneous Transmission in the Decline of COVID-19 Cases During Winter of 2020/2021 in Massachusetts

ImportanceHeterogeneity in transmission of COVID-19 is a significant multiscale phenomenon. However, the role of this heterogeneity in shaping the overall dynamics of disease transmission is not well understood.ObjectiveTo investigate the role of heterogeneous transmission among different towns in Massachusetts in shaping the dynamics of COVID-19 transmission, especially the recent decline during winter of 2020/2021.Design, Setting, ParticipantsAnalysis of COVID-19 data collected and archived by the Massachusetts Department of Public Health.ExposuresThe entire population of the state of Massachusetts is exposed to the virus responsible for COVID-19, to varying degrees. This study quantifies this variation.Main outcome measuresWeekly observations, by town, on confirmed COVID-19 cases in Massachusetts, during the period (April 15th, 2020 to February 9th 2021).ResultsThe relative decline in COVID-19 cases, during January 12th, 2021 to February 9th, 2021, in the group of towns with higher total accumulated cases in the period before January 12th, 2021 is significantly larger than the corresponding relative decline in the group of towns with lower accumulated cases during the same period.Conclusions and RelevanceHeterogeneous nature of transmission is playing a significant role in shaping the rapid recent decline (January 12th to February 9th, 2021) in reported cases in Massachusetts, and probably around the country. These findings are relevant to how we estimate the threshold defining “herd” immunity, suggesting that we should account for effects due to heterogeneity.Key PointsQuestionDoes heterogeneity in disease transmission play a role in shaping the overall dynamics of COVID-19 in Massachusetts, including the recent decline in cases during the 2020/2021 winter.FindingsBased on analysis of data on cases in Massachusetts, the consistent and widespread decline of COVID-19 spread during winter of 2020/2021 (January 12th, 2021 to February 9th, 2021) appears to be shaped to a significant degree by the heterogeneous nature of transmission at the scale of different towns. Towns with a history of high (low) transmission rates during 2020 are experiencing a faster (slower) relative decline.MeaningWe suggest that heterogeneity in transmission of COVID-19 may impact the dynamics of disease transmission including the emergence of “herd” immunity, in line with some recent theoretical studies. This finding deserves some attention from other research groups investigating “herd” immunity, and from federal and state public health authorities concerned with the future evolution of the pandemic.

Contact tracing efficiency, transmission heterogeneity, and accelerating COVID-19 epidemics

Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19. However, manual contact tracing is a time-consuming process and as case numbers increase it takes longer to reach each cases' contacts, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using a stochastic compartment model of SARS-CoV-2. We found that Rt increases sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections is more complicated). Contact tracing efficacy also decreased with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases, and the value of easy access, high testing capacity and rapid turn-around of testing results, as well as outreach efforts to encourage symptomatic infections to be tested immediately after symptom onset.

Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China

COVID-19 caused rapid mass infection worldwide. Understanding its transmission characteristics, including heterogeneity and the emergence of super spreading events (SSEs) where certain individuals infect large numbers of secondary cases, is of vital importance for prediction and intervention of future epidemics. Here, we collected information of all infected cases (135 cases) between 21 January and 26 February 2020 from official public sources in Tianjin, a metropolis of China, and grouped them into 43 transmission chains with the largest chain of 45 cases and the longest chain of four generations. Utilizing a heterogeneous transmission model based on branching process along with a negative binomial offspring distribution, we estimated the reproductive number R and the dispersion parameter k (lower value indicating higher heterogeneity) to be 0.67 (95% CI: 0.54–0.84) and 0.25 (95% CI: 0.13–0.88), respectively. A super-spreader causing six infections was identified in Tianjin. In addition, our simulation allowing for heterogeneity showed that the outbreak in Tianjin would have caused 165 infections and sustained for 7.56 generations on average if no control measures had been taken by local government since 28 January. Our results highlighted more efforts are needed to verify the transmission heterogeneity of COVID-19 in other populations and its contributing factors.

Strongly heterogeneous transmission of COVID–19 in mainland China: local and regional variation

SummaryBackgroundThe outbreak of novel coronavirus disease 2019 (COVID-19) started in the city of Wuhan, China, with a period of rapid initial spread. Transmission on a regional and then national scale was promoted by intense travel during the holiday period of the Chinese New Year. We studied the variation in transmission of COVID-19, locally in Wuhan, as well as on a larger spatial scale, among different cities and even among provinces in mainland China.MethodsIn addition to reported numbers of new cases, we have been able to assemble detailed contact data for some of the initial clusters of COVID-19. This enabled estimation of the serial interval for clinical cases, as well as reproduction numbers for small and large regions.FindingsWe estimated the average serial interval was 4·8 days. For early transmission in Wuhan, any infectious case produced as many as four new cases, transmission outside Wuhan was less intense, with reproduction numbers below two. During the rapid growth phase of the outbreak the region of Wuhan city acted as a hot spot, generating new cases upon contact, while locally, in other provinces, transmission was low.InterpretationCOVID-19 is capable of spreading very rapidly. The sizes of outbreak in provinces of mainland China mainly depended on the numbers of cases imported from Wuhan as the local reproduction numbers were low. The COVID-19 epidemic should be controllable with appropriate interventions.FundingNo specific funding.