Benchmarking the Covid-19 pandemic across countries and states in the USA under heterogeneous testing

Evaluation of self-collected nasal, urine, and saliva samples for molecular detection of SARS-CoV-2 using an EUA approved RT-PCR assay and a laboratory developed LAMP SARS-CoV-2 test

As the SARS-CoV-2 virus spread throughout the world, millions of positive cases of COVID-19 were registered and, even though there are millions of people already vaccinated against SARS-CoV-2, a large part of the global population remains vulnerable to contracting the virus. Massive nasopharyngeal sample collection in Puerto Rico at the beginning of the pandemic was limited by the scarcity of trained personnel and testing sites. To increase SARS-CoV-2 molecular testing availability, we evaluated the diagnostic accuracy of self-collected nasal, saliva, and urine samples using the TaqPath reverse transcription polymerase chain reaction (RT-PCR) COVID-19 kit to detect SARS-CoV-2. We also created a colorimetric loop-mediated isothermal amplification (LAMP) laboratory developed test (LDT) to detect SARS-CoV-2, as another strategy to increase the availability of molecular testing in community-based laboratories. Automated RNA extraction was performed in the KingFisher Flex instrument, followed by PCR quantification of SARS-CoV-2 on the 7500 Fast Dx RT-PCR using the TaqPath RT-PCR COVID-19 molecular test. Data was interpreted by the COVID-19 Interpretive Software from Applied Biosystems and statistically analyzed with Cohen's kappa coefficient (k). Cohen's kappa coefficient (k) for paired nasal and saliva samples showed moderate agreement (0.52). Saliva samples exhibited a higher viral load. We also observed 90% concordance between LifeGene-Biomarks' SARS-CoV-2 Rapid Colorimetric LAMP LDT and the TaqPath RT-PCR COVID-19 test. Our results suggest that self-collected saliva is superior to nasal and urine samples for COVID-19 testing. The results also suggest that the colorimetric LAMP LDT is a rapid alternative to RT-PCR tests for the detection of SARS-CoV-2. This test can be easily implemented in clinics, hospitals, the workplace, and at home; optimizing the surveillance and collection process, which helps mitigate global public health and socioeconomic upheaval caused by airborne pandemics.

Impact of spatiotemporal heterogeneity in COVID-19 disease surveillance on epidemiological parameters and case growth rates

SARS-CoV-2 case data are primary sources for estimating epidemiological parameters and for modelling the dynamics of outbreaks. Understanding biases within case-based data sources used in epidemiological analyses is important as they can detract from the value of these rich datasets. This raises questions of how variations in surveillance can affect the estimation of epidemiological parameters such as the case growth rates. We use standardised line list data of COVID-19 from Argentina, Brazil, Mexico and Colombia to estimate delay distributions of symptom-onset-to-confirmation, -hospitalisation and -death as well as hospitalisation-to-death at high spatial resolutions and throughout time. Using these estimates, we model the biases introduced by the delay from symptom-onset-to-confirmation on national and state level case growth rates (rt) using an adaptation of the Richardson-Lucy deconvolution algorithm. We find significant heterogeneities in the estimation of delay distributions through time and space with delay difference of up to 19 days between epochs at the state level. Further, we find that by changing the spatial scale, estimates of case growth rate can vary by up to 0.13 d^-1. Lastly, we find that states with a high variance and/or mean delay in symptom-onset-to-diagnosis also have the largest difference between the rt estimated from raw and deconvolved case counts at the state level. We highlight the importance of high-resolution case-based data in understanding biases in disease reporting and how these biases can be avoided by adjusting case numbers based on empirical delay distributions. Code and openly accessible data to reproduce analyses presented here are available.

Heterogeneity in testing for infectious diseases

Testing strategies have varied widely between nation states during the COVID-19 pandemic, in intensity as well as methodology. Some countries have mainly performed diagnostic testing while others have opted for mass-screening for the presence of SARS-CoV-2 as well. COVID passport solutions have been introduced, in which access to several aspects of public life requires either testing, proof of vaccination or a combination thereof. This creates a coupling between personal activity levels and testing behaviour which, as we show in a mathematical model, leverages heterogeneous behaviours in a population and turns this heterogeneity from a disadvantage to an advantage for epidemic control.

Learning from Others' Disasters? A Comparative Study of SARS/MERS and COVID-19 Responses in Five Polities

The ability to successfully manage disasters is a function of the extent to which lessons are learned from prior experience. We focus on the extent to which lessons from SARS/MERS have been learned and implemented during the first wave of COVID-19, and the extent to which the source affects governance learning: from a polity's own experience in previous episodes of the same disaster type; from the experience of other polities with regard to the same disaster type; or by cross-hazard learning - transferring lessons learned from experience with other types of disasters. To assess which types of governance learning occurred we analyze the experience of four East Asian polities that were previously affected by SARS/MERS: South Korea, Taiwan, Singapore and Hong-Kong. Their experience is compared with that of Israel. Having faced other emergencies but not a pandemic, Israel could have potentially learned from its experience with other emergencies, or from the experience of others with regard to pandemics before the onset of COVID-19. We find that governance learning occurred in the polities that experienced either SARS or MERS, but not cross-hazard or cross-polity learning. The consequences in the 5 polities at the end of the first six months of Covid-19, reflected by the numbers of infected and deaths, on one hand, and by the level of disruption to normal life, on the other, verifies these findings. Research insights point to the importance of modifying governance structures to establish effective emergency institutions and necessary legislation as critical preparation for future unknown emergencies.