Transmission networks of SARS-CoV-2 in Coastal Kenya during the first two waves: A retrospective genomic study

Article activity feed

1. 

   Version published to 10.7554/elife.71703 on eLife

   Jun 14, 2022
2. 

   eLife

   Feb 22, 2022

   Author Response:

   Reviewer #1 (Public Review):

   • Line 141: It would be beneficial to better understand how the sequenced sample of the population corresponds to the PCR confirmed sample of the population, in order to understand possible selection biases in the sequence data. Could you elaborate on how the composition of sequence PCR confirmed cases matches the composition of PCR confirmed cases, by the demographic characteristics listed in Table 1.

   Early in the pandemic (March-April), we tried to sequence every SARS-CoV-2 positive case diagnosed in our KWTRP laboratory from Coastal Kenya. However, with the sharp increase in the number of identified cases from the month of May 2020 onwards, and a limited in-house sequencing capacity, we changed strategy to sequence only a sub-sample of the identified positives. The criteria for …

   Author Response:

   Reviewer #1 (Public Review):

   • Line 141: It would be beneficial to better understand how the sequenced sample of the population corresponds to the PCR confirmed sample of the population, in order to understand possible selection biases in the sequence data. Could you elaborate on how the composition of sequence PCR confirmed cases matches the composition of PCR confirmed cases, by the demographic characteristics listed in Table 1.

   Early in the pandemic (March-April), we tried to sequence every SARS-CoV-2 positive case diagnosed in our KWTRP laboratory from Coastal Kenya. However, with the sharp increase in the number of identified cases from the month of May 2020 onwards, and a limited in-house sequencing capacity, we changed strategy to sequence only a sub-sample of the identified positives. The criteria for sub-sampling included having a cycle threshold of < 30.0, spatial representation (at county level) and temporal representation (at month level). The consequent number and proportion of samples sequenced across the study period months and across the counties is summarized in Fig. 2C-E with the sample flow provided in Figure 2-figure supplement 1.

   In the revised manuscript we have provided a comparison of the demographic characteristics of the sequenced cases versus non-sequenced cases (shown as Table 2). The participants providing the sequenced and non-sequenced positive samples had a similar gender distribution and similar probabilities of being from either from Wave one or Wave two. However, the distribution of sequenced vs non-sequenced cases differed significantly in age distribution, nationality and travel history. Specifically in the sequenced sample, there were more participants in 30–39 years age bracket compared to the non-sequenced samples, a disproportionately representation of non-Kenyan nationals and persons with a recent international travel history in the sequenced sample.

   • Line 283: I am particularly interested in the observed inter county flows, but it is hard to interpret the numbers. Considering population sizes in each county, what are the phylogenetically observed import rates per 100,000? What are the rate ratios? Based on the observed data, is there any evidence that imports into coastal Kenya occurred statistically significantly through Mombasa?

   We thank the reviewer for these comments.

   In the revised manuscript we have added two new tables (1 & 4) which detail the population size in each of the six Coastal Kenya counties, population density and estimated import/export rates (per 100,000) for the counties.

   The alluvial plots are descriptive regarding genome flows. The underlying data on the pattern of virus movement is inferred using the ancestral state reconstruction which an established phylogenetic approach that has been applied elsewhere to infer SARS-CoV-2 local and global movement (Wilkinson et al, Science 2021, Tegally et al, Nature, 2021).

   The results we obtained from ancestral state reconstruction of Mombasa being a major gateway for variants entering the coastal region of Kenya is consistent with (a) the county showing the highest number circulating of lineages (n=28) compared to the other five remaining counties of Coastal Kenya, (b) approximately half (n=21, 49%) of the detected lineages in coastal Kenya had their first case identified in Mombasa and (c) Mombasa had an early wave of infections compared to the other Coastal counties.

   We are not aware of an approach to consider statistical significance on these plots. The graphical display is based on the observed number events, and we would argue this is more appropriate than presenting absolute rates which would be susceptible to sampling bias.

   Is it possible to account for potential bias in sequence sampling in these calculations, perhaps as done in Bezemer et al AIDS 2021? It should be possible to adjust for the proportion of sequenced individuals in PCR confirmed individuals, and it might also be possible to back calculate infected cases from cumulative reported deaths and to adjust for the proportion of sequenced individuals in infected individuals?

   The reviewer suggests helpful methods to examine sampling bias, but we found this beyond scope here. Our method was based on ancestral location state reconstruction of the dated phylogeny. The approach has been used elsewhere to answer similar questions (Wilkinson et al, Science 2021, Tegally et al, Nature, 2021). The Bezemer paper uses maximum parsimony ancestral state reconstruction algorithm implemented in phyloscanner, and the Bayesian method applied to impute incomplete sampling is applicable to chains of transmission which we have not tried to reconstruct in our analysis.

   Considering my earlier recommendation to document sequence sampling representativeness in Table 1, if Mombasa is found to be oversampled relative to infections, then it might also be helpful to perform sensitivity analyses in which sequences from over-represented locations are down-sampled. Another option might be to consider the approaches considered in de Maio PLOS Comp Bio 2015, or Lemey Nat Comms 2020. Thank you for investigating potential caveats and substantiating your findings in more detail.

   In the revised manuscript we have clarified that our sequenced sample was proportional the number of positive cases reported in the respective Coastal Kenya counties (see-Fig.2E and Table 1).

   The De Maio method uses BASTA (BAyesian STructured coalescent Approximation) into BEAST for purposes of phylogeographic analysis to compare ability to discriminate a zoonotic reservoir vs the implausible alternative cryptic human transmission. Analyses developed from these methods would be valid and interesting to apply to our dataset but would be a major new analysis and beyond the scope of the present paper. We have therefore taken the approach of: a) more clearly acknowledging sampling bias (see below) and b) undertaking sensitivity analyses (Supplementary File 5, see below). Using the larger global background sequence sets selected in a different way (more geographically balanced relative to the first round that was random), we still find that most of the virus introductions into coastal Kenya occurred via Mombasa consistent with our previous analysis.

   The results are consistent with the case numbers in that (i) Mombasa experienced an earlier peak during wave one relative to other counties and (ii) had in total more cases than all the other five counties, and (iii) was commonly the first county of detection for many of the identified lineages in the region. However relative to its population, the border county of Taita Taveta had a higher import rate (13.5. per 100,000 people) compared to that of Mombasa (11.6 per 100,000 people), Table 4

   Observations from our sensitivity analyses (Supplementary File 5) are included in the revised manuscript. We found that the absolute number of estimated viral imports/exports and intercounty transmission events fluctuated depending on the number of Coastal Kenya sequences and size of global comparison dataset but with a clear pattern of (a) counted events increasing with sample size (b) with Mombasa County consistently leading in the number of events; imports or exports.

   • Line 292: The results are of course subject to differences in sequencing rates in each of the countries listed, and differences in reporting of these data.

   This is a valid concern; to mitigate the bias that arises with these differences, unlike in the previous comparison dataset where we randomly selected a specified number of samples per month for each continent, in the revised analysis we have done the selection at country level. We limited the comparison data to maximum of 30 genomes per country per month per year. In this way, countries with high sequencing rates do not become overrepresented in our comparison dataset.

   Some of these biases could be elicited through comparison to international travel data. For example, are the US and England also the top two countries from which most travellers arrive into Kenya? If such additional analyses are out of scope, it seems warranted to either strongly point to the substantial limitations of this analysis, or remove it altogether.

   We concur with the reviewer on the potential bias that could exist in conclusions that arise from inferring sources of importations based on genomic data alone, available from only a few countries. However, vital quality and curated international travel data into Kenya during the study period was not available to us at the time of this analysis. We have therefore agreed to remove the previous analysis on potential origins and destinations of observed Kenya lineages from the revised manuscript.

   What is perhaps striking is that Tanzania is entirely missing from this list, given extensive spread there. Another analysis that could be useful is a comparison of country specific lineage compositions, which might bypass some of the difficulties associated with substantial differences in sequence sampling/reporting rates.

   SARS-CoV-2 genomic data from Tanzania has not been publicly shared to date, and hence is not included. And as indicated above, we have removed the analysis that was trying to infer sources of SARS-CoV-2 importations into Kenya.

   To hypothesize on the potential lineages circulating in Tanzania, we have added a sentence detailing that 5 Pango lineages were identified among the 34 Tanzanian nationals who provided samples that were sequenced: B.1 (n=10), B.1.1 (n=10), B.1.351 (n=8), A (n=5) and A.23.1 (n=1)

   • Line 536: it seems problematic that the data used in the import/export analysis did not contain all available African sequences. Can these be included in the corresponding analysis please.

   In the revised manuscript we have included all accessible, good quality and contemporaneous Africa genomes in the revised manuscript (n=21,150). However due to the huge computational processing power need to process the phylogenetics for such large sequence data sets, we split the analysis into two parts, each with approximately 10,000 genomes (see Figure 3-figure supplement 1).

   Notably with the increased sample size (including the analysis of 390 more genomes from coastal Kenya), we detected far more imports of SARS-CoV-2 into Coastal Kenya compared to our previous analysis (n=280 vs n=69) but only a modest change in exports (n=95 vs n=105) and inter-county virus movement events (239 vs 190).

   Reviewer #2 (Public Review):

   Agoti et al. analyzed SARS-CoV-2 samples collected from infected patients in coastal Kenya, collected between March 2020 and February 2021. This period spans the first two waves of COVID-19 in Kenya, and the authors aimed to understand the lineages circulating throughout the region, in comparison to the virus circulating elsewhere in Kenya and in the world. The manuscript is clearly written, and the figures and results are thorough and well described throughout. These data add to our understanding of COVID-19 in Kenya and in East Africa, and the discussion of how different lineages spread in Kenya (single clusters versus dispersed over several regions) is both interesting and potentially useful for informing public health measures.

   The analyses are well done and excellently presented, but this paper is significantly lacking in a discussion of how sampling bias may affect the stated conclusions. Additionally, the paper focuses almost exclusively on genomic data and fails to closely examine epidemiological factors that may better contextualize the results presented.

   We thank the reviewer for bringing this to our attention, we have added the paragraph below to the revised manuscript.

   “Sampling bias is a potential limitation of this study arising from the fact that (a) demographic characteristics (age distribution, travel history and nationality) of the sequenced versus non-sequenced sub-sample differed significantly, (b) <10% of confirmed SARS-CoV-2 infections in Coastal Kenya were sequenced, prioritizing samples with a Ct value of <30.0 (Table 1); (c) the Ministry of Health case identification protocols were repeatedly altered as the pandemic progressed (Githinji et al., 2021) and (d) sampling intensity across the six Coastal counties differed, probably in part due to varied accessibility of our testing center that is located in Kilifi County (Figure 1A and Table 1). This may have skewed the observed lineage and phylogenetic patterns. To better contextualize the genomic analysis results, close examination of the case metadata is important, but unfortunately there was a lot of the metadata was missing (e.g., travel history, nationality, Table 2) which made it hard to integrate genomic and epidemiological data in an analysis. Although all analyzed genomes had > 80% coverage, very few were complete or near complete (>97.5%, n=344) due to amplicon drop-off or low sample quality and this may have reduced the overall phylogenetic signal.”

   Specifically:

   1. The authors do not discuss the potential effects of sampling on their import/export analyses. For example, they find that the USA and England are in the top six country sources of SARS-CoV-2 importation into coastal Kenya, as well as in the top six country destinations of viral export from the region. These two countries have generated huge numbers of sequences compared to the rest of the world, which may clearly bias these findings. While the authors do evaluate the sensitivity of their analyses by repeating them with different global subsamples, it is unclear if these subsamples corrected for large discrepancies in available data from different parts of the world.

   We concur and appreciate that sampling bias is indeed a common limitation in the type of analysis we have undertaken given the variation in data collection across geographies. Some of the approaches we took to correct for this have been highlighted in our responses to reviewer #1.

   In the revised manuscript, we have undertaken a reanalysis with a larger and more representative dataset at all scales of observation (Figure 3-figure supplement 1). Specifically, for the global dataset, we have revised our sub-sampling script to pick up the comparison dataset uniformly across months and countries for non-African countries. All the available African genomes have been included in our analysis including 605 collected in Kenya outside the coastal regional.

   Similarly, the authors find that new variant introductions were mainly through Mombasa city, but most of the Kenyan sequences were from this region, so it is perhaps unsurprising that more lineages were found there. The authors should repeat their analyses with a more representative global subsample, or at the very least discuss these caveats in the discussion and discuss what other evidence there may be to support their findings.

   Our sequencing rate by county is approximately proportional to the total number of cases seen in the county (Table 1 and Figure 2E). For Coastal Kenya, the revised manuscript included 389 additional genomes from coastal Kenya that became available while the manuscript was under review.

   Thus, in the revised manuscript, we have addressed the valid sampling bias concerns of the reviewers and editor by: (i) increasing the number of analyzed genomes in our dataset for previously under-represented periods and regions, (ii) including contemporaneous Kenyan genomes from outside the coastal counties in our import/export analysis, (iii) including all available Africa genomes into the analysis and selecting a balanced global sub-sample for inclusion into the analysis. In addition, were have also provided a paragraph in the discussion section highlighting sampling bias as a caveat to interpretation of the findings of the current study:

   “The accuracy of the inferred patterns of virus importations to and exportations from coastal Kenya are in part dependent on both the representativeness of our sequenced samples for Coastal Kenya and the comprehensiveness of the comparison data from outside Coastal Kenya. Our sequenced sample was proportional the number of positive cases reported in the respective Coastal Kenya counties (Figure 2E and Table 1). Also, we carefully selected comparison data to optimize chances of observing introductions occurring into the coastal region (e.g. by using all Africa data). But still there remained some important gaps e.g. non-coastal Kenya genomic data was limited (n=605). Despite this, we think the results from ancestral state reconstruction indicating that Mombasa is a major gateway for variants entering coastal Kenya is consistent with (a) the county showing the highest number lineages circulating (n=28) during the study period compared to the other five remaining Coastal counties Kenya, (b) approximately half (n=21, 49%) of the detected lineages in coastal Kenya had their first case identified in Mombasa and (c) Mombasa had an early wave of infections compared to the other Coastal counties and (d) is the most well connected county in the region to the rest of the world (large international seaport and airport and major railway terminus and several bus terminus).”

   1. Restriction measures enforced by the Kenyan government are briefly introduced at the very beginning of the manuscript and then mentioned at the very end as a possible explanation for observed transmission patterns. However, there is very limited discussion of the potential effect of restriction measures throughout, and no formal analyses are presented using this kind of epidemiological information. Adding formal analyses to back up the hypothesis that relaxation of interventions may have driven the second wave of infections would make this paper much stronger and potentially more interesting.

   In the revised manuscript, we have detailed the restriction measures the government of Kenya put in place in the introduction, methods, and results sections and discussed where appropriate on how we think they impacted the observed transmission patterns. We have added Supplementary Table 1 that provides the dates the various measures took effect or were relaxed.

   In a separate piece of work (Brand et al, 2021 published in Science journal, 10.1126/science.abk0414), we investigated the potential drivers of the first three waves of infection observed in Kenya and we have appropriately referenced this in the revised manuscript.

   We feel that additional analyses on the impact of the restriction measures on SARS-CoV-2 epidemiology and the lineage patterns observed are beyond the scope of this work whose focus was primarily genomic epidemiology.

   1. Generally, the text of the manuscript focused on waves of SARS-CoV-2 transmission, while the analyses presented data aggregated by month. A clearer connection between month and wave (particularly visually, on the figures themselves) would aid in interpretation of the data presented.

   This is a valid concern and a good suggestion. In the revised manuscript, for all temporal plots, we have added a line to demarcate when we switched from wave one to wave two period. Similarly, for several analyses, we have provided aggregations by wave period rather than by month.

   1. One of the strengths of this manuscript is the depth to which the authors discuss the detection of specific lineages in coastal Kenya. However, there is limited discussion of these results in the context of when various lineages appeared or disappeared globally, though these details are presented in a table. Discussing the appearance of the various lineages (was it surprising to see a particular lineage at a certain time or in a certain place?) would also improve this manuscript.

   In the revised manuscript, we have compared the patterns of lineage detection locally compared to all Kenya and to all continents in the newly added Figure 3. We have also discussed this aspect for the most frequent 4 lineages in both Wave one and Wave two.

   Read the original source
3. 

   eLife

   Nov 17, 2021

   Evaluation Summary:

   The data and analyses presented in this paper are important for understanding the sources and spread of SARS-CoV-2 across Kenya during the first two waves and are a timely contribution to our understanding of the pandemic in East Africa as a whole. The manuscript provides a clear picture of the viral lineages spreading in coastal Kenya, but sampling biases in the Kenyan and global datasets used make it difficult to evaluate conclusions concerning imports and exports of SARS-CoV-2 into and out of Kenya.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

   Read the original source
4. 

   eLife

   Nov 17, 2021

   Reviewer #1 (Public Review):

   - Line 141: It would be beneficial to better understand how the sequenced sample of the population corresponds to the PCR confirmed sample of the population, in order to understand possible selection biases in the sequence data. Could you elaborate on how the composition of sequence PCR confirmed cases matches the composition of PCR confirmed cases, by the demographic characteristics listed in Table 1.

   - Line 283: I am particularly interested in the observed inter county flows, but it is hard to interpret the numbers. Considering population sizes in each county, what are the phylogenetically observed import rates per 100,000? What are the rate ratios? Based on the observed data, is there any evidence that imports into coastal Kenya occurred statistically significantly through Mombasa? Is it possible to …

   Reviewer #1 (Public Review):

   - Line 141: It would be beneficial to better understand how the sequenced sample of the population corresponds to the PCR confirmed sample of the population, in order to understand possible selection biases in the sequence data. Could you elaborate on how the composition of sequence PCR confirmed cases matches the composition of PCR confirmed cases, by the demographic characteristics listed in Table 1.

   - Line 283: I am particularly interested in the observed inter county flows, but it is hard to interpret the numbers. Considering population sizes in each county, what are the phylogenetically observed import rates per 100,000? What are the rate ratios? Based on the observed data, is there any evidence that imports into coastal Kenya occurred statistically significantly through Mombasa? Is it possible to account for potential bias in sequence sampling in these calculations, perhaps as done in Bezemer et al AIDS 2021? It should be possible to adjust for the proportion of sequenced individuals in PCR confirmed individuals, and it might also be possible to back calculate infected cases from cumulative reported deaths and to adjust for the proportion of sequenced individuals in infected individuals? Considering my earlier recommendation to document sequence sampling representativeness in Table 1, if Mombasa is found to be oversampled relative to infections, then it might also be helpful to perform sensitivity analyses in which sequences from over-represented locations are down-sampled. Another option might be to consider the approaches considered in de Maio PLOS Comp Bio 2015, or Lemey Nat Comms 2020. Thank you for investigating potential caveats and substantiating your findings in more detail.

   - Line 292: The results are of course subject to differences in sequencing rates in each of the countries listed, and differences in reporting of these data. Some of these biases could be elicited through comparison to international travel data. For example, are the US and England also the top two countries from which most travellers arrive into Kenya? If such additional analyses are out of scope, it seems warranted to either strongly point to the substantial limitations of this analysis, or remove it altogether. What is perhaps striking is that Tanzania is entirely missing from this list, given extensive spread there. Another analysis that could be useful is a comparison of country specific lineage compositions, which might bypass some of the difficulties associated with substantial differences in sequence sampling/reporting rates.

   - Line 536: it seems problematic that the data used in the import/export analysis did not contain all available African sequences. Can these be included in the corresponding analysis please.

   Read the original source
5. 

   eLife

   Nov 17, 2021

   Reviewer #2 (Public Review):

   Agoti et al. analyzed SARS-CoV-2 samples collected from infected patients in coastal Kenya, collected between March 2020 and February 2021. This period spans the first two waves of COVID-19 in Kenya, and the authors aimed to understand the lineages circulating throughout the region, in comparison to the virus circulating elsewhere in Kenya and in the world. The manuscript is clearly written, and the figures and results are thorough and well described throughout. These data add to our understanding of COVID-19 in Kenya and in East Africa, and the discussion of how different lineages spread in Kenya (single clusters versus dispersed over several regions) is both interesting and potentially useful for informing public health measures.

   The analyses are well done and excellently presented, but this paper is …

   Reviewer #2 (Public Review):

   Agoti et al. analyzed SARS-CoV-2 samples collected from infected patients in coastal Kenya, collected between March 2020 and February 2021. This period spans the first two waves of COVID-19 in Kenya, and the authors aimed to understand the lineages circulating throughout the region, in comparison to the virus circulating elsewhere in Kenya and in the world. The manuscript is clearly written, and the figures and results are thorough and well described throughout. These data add to our understanding of COVID-19 in Kenya and in East Africa, and the discussion of how different lineages spread in Kenya (single clusters versus dispersed over several regions) is both interesting and potentially useful for informing public health measures.

   The analyses are well done and excellently presented, but this paper is significantly lacking in a discussion of how sampling bias may affect the stated conclusions. Additionally, the paper focuses almost exclusively on genomic data and fails to closely examine epidemiological factors that may better contextualize the results presented. Specifically:

   1. The authors do not discuss the potential effects of sampling on their import/export analyses. For example, they find that the USA and England are in the top six country sources of SARS-CoV-2 importation into coastal Kenya, as well as in the top six country destinations of viral export from the region. These two countries have generated huge numbers of sequences compared to the rest of the world, which may clearly bias these findings. While the authors do evaluate the sensitivity of their analyses by repeating them with different global subsamples, it is unclear if these subsamples corrected for large discrepancies in available data from different parts of the world. Similarly, the authors find that new variant introductions were mainly through Mombasa city, but most of the Kenyan sequences were from this region, so it is perhaps unsurprising that more lineages were found there. The authors should repeat their analyses with a more representative global subsample, or at the very least discuss these caveats in the discussion and discuss what other evidence there may be to support their findings.

   2. Restriction measures enforced by the Kenyan government are briefly introduced at the very beginning of the manuscript and then mentioned at the very end as a possible explanation for observed transmission patterns. However, there is very limited discussion of the potential effect of restriction measures throughout, and no formal analyses are presented using this kind of epidemiological information. Adding formal analyses to back up the hypothesis that relaxation of interventions may have driven the second wave of infections would make this paper much stronger and potentially more interesting.

   3. Generally, the text of the manuscript focused on waves of SARS-CoV-2 transmission, while the analyses presented data aggregated by month. A clearer connection between month and wave (particularly visually, on the figures themselves) would aid in interpretation of the data presented.

   4. One of the strengths of this manuscript is the depth to which the authors discuss the detection of specific lineages in coastal Kenya. However, there is limited discussion of these results in the context of when various lineages appeared or disappeared globally, though these details are presented in a table. Discussing the appearance of the various lineages (was it surprising to see a particular lineage at a certain time or in a certain place?) would also improve this manuscript.

   Read the original source
6. 

   Version published to 10.1101/2021.07.01.21259583v2 on medRxiv

   Nov 15, 2021
7. 

   ScreenIT

   Jul 10, 2021

   SciScore for 10.1101/2021.07.01.21259583: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   NIH rigor criteria are not applicable to paper type.

   Table 2: Resources

   Software and Algorithms
   Sentences	Resources
   Quick non-bootstrapped neighbor joining trees were created in SEAVIEW to identify any aberrant sequences that were henceforth discarded.	SEAVIEW suggested: (SeaView, RRID:SCR_015059)
   TempEst v1.5.3 was used to assess the presence of a molecular clock signal in analysed data and linear regression of root-to-tip genetic distances against sampling dates plotted.	TempEst suggested: (TempEst, RRID:SCR_017304)
   Using the date and location annotated tree topology, we counted the number of transitions between and within Coastal Kenya counties and the rest of the world and plotted this using ggplot2 …

   SciScore for 10.1101/2021.07.01.21259583: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   NIH rigor criteria are not applicable to paper type.

   Table 2: Resources

   Software and Algorithms
   Sentences	Resources
   Quick non-bootstrapped neighbor joining trees were created in SEAVIEW to identify any aberrant sequences that were henceforth discarded.	SEAVIEW suggested: (SeaView, RRID:SCR_015059)
   TempEst v1.5.3 was used to assess the presence of a molecular clock signal in analysed data and linear regression of root-to-tip genetic distances against sampling dates plotted.	TempEst suggested: (TempEst, RRID:SCR_017304)
   Using the date and location annotated tree topology, we counted the number of transitions between and within Coastal Kenya counties and the rest of the world and plotted this using ggplot2 v3.3.3.	ggplot2 suggested: (ggplot2, RRID:SCR_014601)

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   Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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   Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

   The key limitations of our analysis include, first, the samples we sequenced were only those available to us through the rapid response teams (RRTs) whose case identification protocols were altered at the different study phases following guidance from the MoH. Second, we only sequenced ∼10% of the positive samples identified in our laboratory, the majority from the introduction phase and were prioritizing samples with a Ct value of <30.0. Third, the sampling across the six Coastal counties was not uniform, probably in part due to varied distance from our testing centre located in Kilifi County but also the total number of positive cases varied between the counties. In conclusion, we show that the first two SARS-CoV-2 waves in Coastal Kenya observed transmission of both newly introduced variants and potentially locally evolved variants. New variant introductions appeared to mainly occur through Mombasa city. Strikingly, only a limited number of the many introduced variants progressed to transmit extensively perhaps due to ongoing public health interventions e.g., screening at ports of entry, case isolation and quarantining of contacts of cases. Unlike in the global contemporaneous sample, we did not find evidence of extensive local transmission of the global VOC during wave two. Thus, we infer that it is more likely that the relaxation of some of the interventions (e.g., reopening of learning institutions, airspace, bars and restaurants) that drove the second wave of infection...

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   Results from TrialIdentifier: No clinical trial numbers were referenced.

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   Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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   Results from JetFighter: We did not find any issues relating to colormaps.

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   Results from rtransparent:

   • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
   • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
   • No protocol registration statement was detected.

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   Results from scite Reference Check: We found no unreliable references.

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   Read the original source
8. 

   Version published to 10.1101/2021.07.01.21259583v1 on medRxiv

   Jul 7, 2021