COVID-19 cases in Kenya are accelerating rapidly. New cases have increased 300% month-over-month since April of this year while global and regional media have reported on the economic toll of stringent lock-down measures and heavy-handed government practices.

肯尼亞的COVID-19病例正在Swift增加。 自今年4 月以來， 新案件數量環比增長了300％ ，而全球和區域媒體都報道了嚴格的封鎖措施和嚴厲的政府做法 造成的經濟損失 。

In previous posts, we explored how Africa and the rest of the world have struggled to understand the full extent of the pandemic on the continent. To date, there is no comprehensive analysis of how the crisis is playing out in sub-Saharan Africa and until now, the global community has been guilty of using limited data (cases and deaths) in most analyses, presenting a lopsided and incomplete picture of the pandemic in Africa.

在以前的文章中，我們探討了非洲和世界其他國家如何努力了解該大陸的整個流行病范圍。 迄今為止，尚無關于撒哈拉以南非洲危機如何蔓延的全面分析，直到現在，全球社會一直在大多數分析中使用有限的數據(案件和死亡)，這使該組織歪曲而又不完整。非洲大流行。

To address this gap we have developed a bespoke analysis that integrates typical epidemiological datasets with population mobility data from Google Mobility reports and policy data from Oxford. We explore these data sources separately and in combination with one another through in an illustrative use case for Kenya. We then go a step further by comparing these data sources to a recent pre-print seroprevalence study, which adds deeper dimension to testing positivity and antibody presence in Kenyan populations.

為了解決這一差距，我們開發了定制分析，將典型的流行病學數據集與Google Mobility報告中的人口流動數據以及牛津大學的政策數據相集成。 在肯尼亞的一個示例性用例中，我們將分別或相互結合地探索這些數據源。 然后，我們通過將這些數據源與最近的印刷前血清陽性率研究進行比較 ，從而進一步走了一步，該研究為檢測肯尼亞人群的陽性和抗體存在增加了更深的層面。

For the first part of this analysis, we used three publicly-available datasets to construct a composite snapshot of the COVID-19 pandemic within Kenya from February 10th — August 10th.

對于此分析的第一部分，我們使用了三個公開可用的數據集，構建了2月10日至8月10日肯尼亞境內COVID-19大流行的復合快照。

1. COVID-19 Community Mobility Reports

   COVID-19社區流動性報告

2. Our World in Data

   數據世界

3. Oxford COVID-19 Government Response Tracker

   牛津COVID-19政府回應追蹤器

Comparing these data side by side, we found the following:

并排比較這些數據，我們發現：

1.政府政策只在短時間內限制了日常活動，肯尼亞人繼續上班 (1. Government policies only restricted daily movements for a short time and Kenyans continued to go to work)

When restrictions (e.g. public events, gatherings, educational institutions) were implemented in early March, data showed a decline in mobility to grocery stores and pharmacies, parks, retail and transit stations.

3月初實施限制措施(例如公共活動，聚會，教育機構)時，數據顯示，前往雜貨店和藥房，公園，零售和公交車站的出行人數有所減少。

In June, there was a gradual return to baseline mobility while many restrictions were still in place. Comparable rise in mobility for residences is not observed around the same period (early March). Instead, the rise in mobility for places of residence is much more gradual. This may be due to non-residence locations (grocery stores, parks, etc.) being easier to identify using mobile phone data compared to an individual’s “home.”

6月份，基線流動性逐漸恢復，但仍存在許多限制。 在同一時期(3月上旬)，沒有觀察到居民流動性的可比增長。 取而代之的是，居住地流動性的增加是漸進的。 這可能是由于與個人的“家”相比，使用手機數據更容易識別非居住位置(雜貨店，公園等)。

There is also a cyclical pattern for workplace mobility, indicating that many Kenyans were not working from home, likely because their job was not conducive to remote work. Another possible explanation is that, while the Kenyan government provided some financial relief, it may not have been sufficient to fully supplement Kenyans’ financial requirements.

還有一種周期性的工作場所流動模式，表明許多肯尼亞人不是在家工作，可能是因為他們的工作不利于遠程工作。 另一個可能的解釋是， 雖然肯尼亞政府提供了一些財政救濟， 但可能不足以 完全補充肯尼亞人的財政需求。

2. 每天新發病例和死亡人數Swift增加，但化驗數量也在增加 (2. Daily new cases and deaths are rapidly rising — but so are the number of tests)

Throughout March and April, daily new cases in Kenya remained extremely low until approximately mid- to late-May when they began to rise. COVID-19 deaths were also generally low until June when they began to rise. Notably, up until June, 60% of days did not have a single COVID death, but since June 1st, there has not been a single day without a COVID-19 death and August is seeing average daily deaths in the double digits.

在整個3月和4月，肯尼亞的每日新病例一直保持極低的水平，直到大約5月中下旬才開始上升。 直到6月開始上升之前，COVID-19的死亡人數也普遍較低。 值得注意的是，到6月為止，有60％的日子沒有發生過一次COVID死亡， 但是自6月1日以來，沒有一天沒有發生過COVID-19死亡，而8月的平均每日死亡人數達到了兩位數。

What, however, was the testing capacity was during this time period and how could it have changed over the past few months? We see that the pattern of new daily COVID-19 tests follows a very similar pattern to that of both cases and deaths. While there is some missing testing data in the early months, there is a clear association between increasing case and death detection and tests done.

但是，這段時間內的測試容量是多少？過去幾個月中它有什么變化？ 我們看到，每天進行新的COVID-19測試的模式與病例和死亡的模式非常相似。 盡管在最初的幾個月中缺少一些測試數據，但是在增加的病例和死亡檢測與完成的測試之間存在明顯的關聯。

3.盡管肯尼亞政府最初實施了嚴格的政策，但短暫的平靜之后，案件繼續增加 (3. Despite stringent policies initially implemented by the Kenyan government, after a short lull, cases continue to rise)

Another critical insight for understanding a nation’s COVID-19 response is the policy landscape. The Oxford COVID-19 Government Response Tracker systematically collects, analyzes and presents policy responses over time, in countries around the globe, across several indicators.

了解一個國家對COVID-19的回應的另一個重要見解是政策格局。 牛津COVID-19政府React跟蹤器在全球范圍內跨多個指標系統地收集，分析并提出了隨時間推移的政策React。

From these data, we can see that public information campaigns were the first policy tool implemented in Kenya in February, suggesting that the government acknowledged the threat of COVID-19 early on. The next wave of policies came into play on March 14th — the date of Kenya’s first confirmed COVID case. On that date, we see sweeping regulations on international travel, public events, gatherings, workplaces, and schools.

從這些數據中，我們可以看到，公共宣傳運動是2月份在肯尼亞實施的第一個政策工具，這表明政府早就意識到了COVID-19的威脅。 新一輪政策于3月14日生效-肯尼亞首例確診的COVID案發生之日。 那天，我們看到了有關國際旅行，公共活動，聚會，工作場所和學校的全面法規。

Viewing policy data compared against testing data, we see that more liberal testing policies were put in place the first week of May. In April, the average number of new daily tests was about 650 and after testing policy expanded, average new tests in May tripled to nearly 1,900. We can see an additional surge in new daily tests from June to July when contact tracing policies intensified, resulting in an increase from 3,000 to 4,300 new tests per day on average.

查看政策數據與測試數據的比較，我們發現5月第一周實施了更為寬松的測試政策。 4月份，平均每日新測試次數約為650次，并且隨著測試政策的擴大，5月份的平均新測試次數增加了兩倍，達到近1900次。 從6月到7月，隨著接觸追蹤政策的加強，我們發現新的日常測試會進一步激增，平均每天新測試從3,000個增加到4,300個。

Finally, we draw attention to the stay-at-home policy, gradually enacted in late March with a full lockdown in place by early April — lasting through June 22nd. From Google Mobility data, this policy coincides with a nearly 50% reduction in mobility during roughly the same time period. Notably however, mobility began to decline well ahead of a stay-at-home order and began returning to normal levels before the lockdown was truly lifted. This indicates that public information campaigns and individual behaviors were critically important early in the epidemic, while the economic demand to return to work and normal life activities led to more mobility in June and July — despite policies recommending otherwise.

最后，我們提請注意“在家辦公”政策，該政策于3月下旬逐步頒布，并在4月初(到6月22日)全面鎖定。 根據Google Mobility數據，此政策與大致在同一時間段內的移動性降低了近50％相吻合。 但是，值得注意的是，在居家訂購之前，流動性開始大大下降，并在真正解除鎖定之前開始恢復到正常水平。 這表明，在流行病初期，開展公共宣傳活動和個人行為至關重要，而在經濟方面，恢復工作和正常生活活動的需求在6月和7月導致了更多的流動性，盡管政策另有建議。

4.一項新的血清陽性率研究顯示SARS-CoV-02抗體水平升高 (4. A new seroprevalence study shows rising levels of SARS-CoV-02 antibodies)

Despite available data on mobility, new cases, deaths, tests and policy, understanding the true underlying epidemiology of COVID-19 remains elusive given severe limitations in testing and potential biases arising from who gets tested, has severe disease and dies.

盡管有關于流動性，新病例，死亡，檢測和政策的可用數據，但鑒于檢測的嚴重局限性以及由于接受檢測的人，患有嚴重疾病和死亡的潛在偏見，對COVID-19真正的潛在流行病學的了解仍然難以捉摸。

New data from a preprint (not yet peer reviewed) study on the seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors may provide clues as to the true trends of incidence and prevalence of COVID-19 in Kenya.

預印本(尚未經過同行評審)研究中 的抗SARS-CoV-2 IgG抗體血清陽性率的 新數據 肯尼亞的獻血者 可能提供有關肯尼亞COVID-19發生率和患病率真實趨勢的線索。

A seroprevalence study uses serology tests to identify people in a population that have antibodies against an infectious disease in order to estimate the percentage of the population that may have been infected. In addition, it shows how an infection progresses through the population over time.

血清陽性率研究使用血清學檢測來鑒定人群中具有抗傳染病抗體的人群，以估計可能已感染人群的百分比。 此外，它還顯示了隨著時間的推移，感染如何在整個人群中發展。

In Kenya, the study population’s seroprevalence hovered around 5% throughout May and has been slowly rising since early June.

在肯尼亞，整個五月份，研究人群的血清陽性率徘徊在5％左右，自六月初以來一直在緩慢上升。

Similarly, Kenya’s test positivity rate — the percentage of all tests conducted that come back positive — was also increasing in early June. The test positivity rate remained below 10% until early June and increased dramatically in early July.

同樣，肯尼亞的考試陽性率(所進行的所有考試中恢復陽性的百分比)在6月初也有所增加。 直到6月初，測試陽性率仍低于10％，7月初急劇上升。

Combining our four data sources including the seroprevalence data indicates that COVID-19 incidence has been rising in Kenya as early as May. However, stay-at-home orders were not lifted until the end of June. So why did COVID-19 begin spreading so early and so rapidly?

結合我們的四個數據來源(包括血清陽性率數據)，表明肯尼亞最早5月份的COVID-19發病率一直在上升。 但是，直到6月底才取消了全屋服務訂單。 那么，為什么COVID-19這么早又如此Swift地開始傳播？

1. 待在家里的命令和禁止社交聚會會影響出行能力，但個人行為也會造成影響 (1. Stay-at-home orders and bans on social gatherings have an impact on mobility — but so do individual behaviors)

Stay-at-home orders and restrictions on social events and gatherings likely had a significant impact on population mobility. There was also a clear return to more normal mobility before lockdown was lifted and even while social distancing recommendations remained in place.

留在家里的訂單以及對社交活動和聚會的限制可能會對人口流動產生重大影響。 在解除鎖定之前，甚至在保持疏遠社交的建議仍然存在的情況下，也明顯恢復了正常的出行。

The likely explanation is that people needed to meet their subsistence and household income needs and economic support from the government wasn’t enough. To sustain individual behavior change over periods longer than a month, nations must provide adequate and effective economic and psychosocial support if maintaining limited contact is a priority.

可能的解釋是，人們需要滿足他們的生活和家庭收入需求，而政府的經濟支持還不夠。 為了在一個多月的時間內維持個人行為的改變，如果必須優先保持有限的聯系，各國必須提供充分有效的經濟和社會心理支持。

2.政府的測試政策至關重要 (2. Government policies on testing are critically important)

The expansion of testing and contact tracing policies resulted in drastic increases in average daily testing levels, increasing capacity by thousands of tests per day.

測試和聯系人跟蹤策略的擴展導致平均每日測試水平急劇增加，每天增加了數千個測試的容量 。

This highlights the importance of government action in procuring commodities and supporting supply chains to ensure the availability of treatments, hospital supplies and infrastructure and personal protective equipment.

這突出了政府在采購商品和支持供應鏈以確保治療，醫院用品，基礎設施和個人防護設備的可用性方面采取行動的重要性。

3.測試率和測試陽性率增加。 (3. Testing rates and test positivity increased.)

Although testing rates did increase dramatically over the time period, we also saw that test positivity increased, suggesting that the increase in tests alone did not account for the increase in cases.

盡管在此期間測試率確實顯著增加，但我們也看到測試陽性率增加，這表明僅測試的增加并不能說明病例數的增加。

Along with the trending seroprevalence data, it is safe to assume that underlying incidence was (and is still) on the rise. Only by taking each data source into consideration simultaneously can we draw accurate conclusions about the epidemiology of COVID and inform policy accordingly.

連同趨勢的血清流行率數據一起，可以安全地假設潛在的發病率正在(并且仍在)上升。 只有同時考慮每個數據源，我們才能得出關于COVID流行病學的準確結論，并相應地告知政策。

Overall, we saw that increased mobility, despite stringent country policies or increased testing, is likely the primary reason for the increase of COVID cases in Kenya. Renewed focus on policies which mitigate economic and psychosocial harm, while enabling responsible and safe behavior, will be key to sustaining a long-term response to the crisis.

總體而言，我們看到，盡管采取了嚴格的國家政策或加大了測試力度，但流動性的增加可能是肯尼亞COVID病例增加的主要原因。 重新關注減輕經濟和社會心理傷害的政策，同時實現負責任和安全的行為，將是維持長期應對危機的關鍵。

肯尼亞接下來要做什么？ (What’s next for Kenya?)

Our goal with this analysis was to identify underlying epidemic patterns from atypical combinations of publicly available data sources. In doing so, we reveal a data-driven, multidimensional view of Kenya’s experience with the COVID-19 pandemic.

我們進行此分析的目的是從可公開獲得的數據源的非典型組合中識別潛在的流行病模式。 通過這樣做，我們揭示了肯尼亞在COVID-19大流行中的經驗的數據驅動的多維視圖。

As a global community, we have a mandate to think of fresh questions, hypothesize answers and generate new leads that may help slow or stop the ongoing crisis. In a previous post, we outlined a balanced approach that may offer another way to slow the spread of COVID-19 across the African continent.

作為一個全球社區，我們負有思考新問題，假設答案并產生新線索的任務，這可能有助于減緩或阻止持續的危機。 在上一篇文章中，我們概述了一種平衡的方法 ，它可能提供另一種方法來減緩COVID-19在非洲大陸的擴散。

Kenya could benefit from implementing hyper-targeted, data-driven strategies that take into account their population’s response to top-down policies. Knowing the location and frequency of population movement, combined with regular review of seroprevalence rates and typical testing data, could lead to clearer informational campaigns, informed placement of testing sites or public-safety strategies in key areas.

肯尼亞可以從實施針對性強，數據驅動的戰略中受益，這些戰略考慮了其人口對自上而下政策的React。 了解人口流動的地點和頻率，并定期檢查血清陽性率和典型的檢測數據，可以導致開展更清晰的信息運動，知情的檢測地點布置或關鍵地區的公共安全策略。

Data is our most powerful tool against COVID-19. By leveraging its data, Kenya can implement better and more proactive policies and strategies to gain an edge against the pandemic.

數據是我們針對COVID-19的最強大工具。 通過利用其數據，肯尼亞可以實施更好，更主動的政策和策略，以取得對抗大流行的優勢。

This analysis was led by Cooper/Smith’s resident interns Nathali Gunawardena and Deborah Chan, Masters of Science in Public Health students at McGill University and supervised by Dylan Green, MPH, PhC in Epidemiology at University of Washington. Nathali holds a BS in Biomedical Science with a minor in Psychology with extensive research experience on maternal and child health in Africa. Deborah is joint researcher and clinician as a Nurse with quantitative research interests in global health. Together, they have been invaluable members of our team as we continue to attempt to make sense of clinical and epidemiological data during the COVID-19 pandemic.

這項分析是由Cooper / Smith的常住實習生Nathali Gunawardena和Deborah Chan(麥吉爾大學的公共衛生專業碩士)以及華盛頓大學流行病學博士學位的Dylan Green指導的。 納塔利(Nathali)持有生物醫學科學學士學位，輔修心理學，在非洲的母嬰健康方面擁有豐富的研究經驗。 Deborah是一名聯合研究員和臨床醫生，是一名護士，在全球衛生領域擁有定量研究興趣。 他們在一起一直是我們團隊的寶貴成員，因為我們繼續嘗試在COVID-19大流行期間理解臨床和流行病學數據。