729 lines
60 KiB
Markdown
729 lines
60 KiB
Markdown
<div class="section">
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<div>
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<iframe id="splash" width="960" height="480" src="banners/splash.html"></iframe>
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<div style="top: 70px;font-size: 75px;font-weight: bold;">
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Что будет дальше?
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</div>
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<div style="font-weight: 500;top: 140px;left: 10px;font-size: 29px;">
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Прогнозы COVID-19, объяснённые на игровых симуляциях
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</div>
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<div style="font-weight: 100;top: 189px;left: 10px;font-size: 19px;line-height: 21px;">
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<b>
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🕐 Время чтения/игры: 30 минут
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·
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</b>
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<a href="https://scholar.google.com/citations?user=_wHMGkUAAAAJ&hl=en">Marcel Salathé</a>
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(эпидемиолог)
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&
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<a href="https://ncase.me/">Nicky Case</a>
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(арт/код)
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</div>
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</div>
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</div>
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"Единственное, чего нам следует бояться, это страха" -- это глупый совет.
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Конечно не надо скупать туалетную бумагу, но если власти боятся самого страха, они будут преуменьшать опасности, чтобы "избежать паники". Проблема не в страхе, а в том, куда мы его *направляем*. Страх даёт нам энергию бороться с опасностями и приготовиться к будущим угрозам.
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Если честно, мы (Marcel, эпидемиолог, и Nicky, арт/код) беспокоимся. Бьёмся об заклад, вы тоже! Поэтому мы направили наш страх на то, чтобы сделать эти **игровые симуляции**, чтобы *вы* могли направить свой страх на понимание:
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* **Последние несколько месяцев** (эпидемиологический ликбез, модель SEIR, R и R<sub>0</sub>)
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* **Следующие несколько месяцев** (карантин, отслеживание контактов, маски)
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* **Следующие несколько лет** (утрата иммунитета? отсутствие вакцины?)
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Эта статья (опубликована 01.05.2020. Кликните на ссылку!→[^timestamp]) даст вам надежду *и* страх. Чтобы победить COVID-19 **и сохранить здоровье и финансовое положение**, нам нужны оптимизм, чтобы придумать план, и пессимизм, чтобы придумать "План Б". Как сказала Глэдис Бронвис Стерн: *“Оптимист придумал самолёт, пессимист — парашют.”*
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[^timestamp]: Эти сноски содержат источники, ссылки или бонусные комментарии. Как здесь!
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**Эта статья была опубликована 01.05.2020.** Многие детали устареют, но мы уверены, что эта статья покрывает 95% вариантов развития событий, а эпидемиологический ликбез будет полезен всегда.
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(Обновление от 15 Мая: Добавлены цитаты для "1 из 20 инфицированных госпитализируется" и "0.5% инфицированных умирают")
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Пристегните ремни: мы входим в зону турбулентности!
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<div class="section chapter">
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<div>
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<img src="banners/curve.png" height=480 style="position: absolute;"/>
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<div>Последние несколько месяцев</div>
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</div>
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</div>
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Пилоты используют симуляторы полёта, чтобы понять, как не разбить самолёт.
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**Эпидемиологи используют симуляторы эпидемии, чтобы понять, как не разбить человечество.**
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Давайте сделаем очень, *очень* простой "эпидемический симулятор полёта"! В этой симуляции <icon i></icon> Заразные люди могут превратить <icon s></icon> Уязвимых людей в <icon i></icon> Заразных:
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![](pics/spread.png)
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Оценивают, что *в начале* вспышки COVID-19 вирус переходил с <icon i></icon> на <icon s></icon> каждые 4 дня *в среднем*.[^serial_interval] (хотя данные варьируются)
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[^serial_interval]: “Средний [серийный] интервал составил 3.96 days (95% CI 3.53–4.39 days)”. [Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Ancel Meyers L](https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article) (Дисклеймер: статьи с ранним доступом могут отличаться от финальной версии)
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Если мы симулируем сценарий *только* удвоения каждые 4 дня, начиная со всего 0.001% <span class="nowrap"><icon i></icon></span>, что случится?
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**Нажмите "Start"! Вы сможете перезапустить игру с другими настройками:** (технические оговорки: [^caveats])
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[^caveats]: **Помните: все эти симуляции упрощённые и нужны для образовательных целей.**
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Одно упрощение: Когда вы говорите симуляции "Инфицировать 1 человека каждые X дней", на самом деле она увеличивает количество заражённых на 1/X каждый день. В следующих симуляциях появится настройка: "Период болезни X дней", она аналогично уменьшает количество заражённых на 1/X каждый день.
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Это *не* одно и то же, но довольно близко, и для образовательных целей это понятнее, чем устанавливать показатели передачи вируса и выздоровления напрямую.
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<div class="sim">
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<iframe src="sim?stage=epi-1" width="800" height="540"></iframe>
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</div>
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Это **кривая экспоненциального роста.** Начинается медленно, а потом взлетает. От "Да это просто грипп" до "Действительно, грипп не выливается в *массовые захоронения в богатых городах*".
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![](pics/exponential.png)
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Но эта симуляция неправильная. Экспоненциальный рост, к нашему счастью, не может продолжаться вечно. Одна из причин, которые мешают вирусу распространяться, это то, что у других *уже* есть вирус.
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![](pics/susceptibles.png)
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Чем больше вокруг <span class="nowrap"><icon i></icon></span>, тем быстрее <span class="nowrap"><icon s></icon></span> превращаются в <span class="nowrap"><icon i></icon></span>, **но чем меньше вокруг <span class="nowrap"><icon s></icon></span>, тем *медленнее* <span class="nowrap"><icon s></icon></span> становятся <span class="nowrap"><icon i></icon></span>.**
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Как это меняет рост эпидемии? Давайте выясним:
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<div class="sim">
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<iframe src="sim?stage=epi-2" width="800" height="540"></iframe>
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</div>
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Это S-образная **логистическая кривая.** Она медленно растёт, взлетает, а потом снова замедляется.
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Но эта симуляция *опять* неправильная. Мы упускаем то, что
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<icon i></icon> Заразные люди рано или поздно перестают быть заразными потому что 1) выздоравливают, 2) "выздоравливают" с непоправимым ущербом для лёгких, или 3) умирают.
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Для простоты, давайте считать, что все
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<icon i></icon> Заразные люди становятся <icon r></icon> Выздоровевшими. (Просто помните, что на самом деле некоторые из них мертвы.) <span class="nowrap"><icon r></icon></span> не могут быть заражены снова, и давайте – *пока!* – считать, что иммунитет сохраняется на всю жизнь.
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В случае COVID-19 оценивают, что человек <icon i></icon> Заразен *в среднем* 10 дней.[^infectiousness] Это значит, что некоторые выздоровеют быстрее 10 дней, а некоторые медленнее. **Вот как это выглядит, если симуляция начинается с 100% <span class="nowrap"><icon i></icon></span>:**
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[^infectiousness]: “The median communicable period \[...\] was 9.5 days.” [Hu, Z., Song, C., Xu, C. et al](https://link.springer.com/article/10.1007/s11427-020-1661-4) Да, мы знаем, что "медиана" -- это не то же самое, что "среднее", но для образовательного упрощения это достаточно близко.
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<div class="sim">
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<iframe src="sim?stage=epi-3" width="800" height="540"></iframe>
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</div>
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Это противоположность экспоненциального роста, **кривая экспоненциального затухания.**
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Что случится, если мы запустим S-образный логистический рост *с* выздоровлением?
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![](pics/graphs_q.png)
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Давайте выясним.
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<b style='color:#ff4040'>Красная кривая</b> -- это *текущие* больные <span class="nowrap"><icon i></icon>,</span>
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<b style='color:#999999'>Серая кривая</b> -- это *общее количество* случаев (текущие больные и выздоровевшие <span class="nowrap"><icon r></icon>),</span>
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Начиная со всего 0.001% <span class="nowrap"><icon i></icon>:</span>
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<div class="sim">
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<iframe src="sim?stage=epi-4" width="800" height="540"></iframe>
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</div>
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*Именно отсюда* берётся та самая знаменитая кривая! Это не гауссов колокол, и даже не "логнормальная" кривая. У неё нет имени. Но вы видели её миллион раз и просили её сгладить.
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Это **модель SIR**,[^sir]
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(<icon s></icon>**S**usceptible <icon i></icon>**I**nfectious <icon r></icon>**R**ecovered)
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*вторая* по важности идея в эпидемиологическом ликбезе:
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[^sir]: Для более подробного объяснения модели SIR, смотри [the Institute for Disease Modeling](https://www.idmod.org/docs/hiv/model-sir.html#) и [Wikipedia](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model)
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![](pics/sir.png)
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**ВНИМАНИЕ: Симуляции, которые используются в планировании политики сильно, *сильно* сложнее, чем наша!** Но модель SIR всё равно может объяснить общие закономерности, даже если она и упускает нюансы.
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На самом деле, давайте добавим один нюанс: перед тем как человек из <icon s></icon> превращается в <span class="nowrap"><icon i></icon></span>, он вначале становится <icon e></icon> Латентно инфицированным. Это значит, что у него есть вирус, но он его не может передать – *заражённый*, но ещё не *заразный*.
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![](pics/seir.png)
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(Это вариант называется **модель SEIR**[^seir], где "E" значит <icon e></icon> "Exposed", Латентно инфицированный.)
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[^seir]: Больше технических деталей по модели SEIR смотри на [the Institute for Disease Modeling](https://www.idmod.org/docs/hiv/model-seir.html) и [Wikipedia](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model)
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Для COVID-19 оценивается, что человек остаётся <icon e></icon> заражённым-но-пока-не-заразным 3 дня *в среднем*. [^latent] Что случится, если мы добавим это в симуляцию?
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[^latent]: “Assuming an incubation period distribution of mean 5.2 days from a separate study of early COVID-19 cases, we inferred that infectiousness started from 2.3 days (95% CI, 0.8–3.0 days) before symptom onset” (перевод: Симптомы начинаются на пятый день, а заразным человек становится за 2 дня до этого = заразным человек становится на третий день) [He, X., Lau, E.H.Y., Wu, P. et al.](https://www.nature.com/articles/s41591-020-0869-5)
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<b style='color:#ff4040'>Красная <b style='color:#FF9393'>+ Розовая</b> кривая</b> -- это *носители* (Заразные <icon i></icon> + Латентно инфицированные <span class="nowrap"><icon e></icon>),</span>
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<b style='color:#888'>Серая кривая</b> -- это *общее* количество (носители + Выздоровевшие <span class="nowrap"><icon r></icon>):</span>
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<div class="sim">
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<iframe src="sim?stage=epi-5" width="800" height="540"></iframe>
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</div>
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Не сильно-то и поменялось! То как долго человек инфицирован латентно <icon e></icon> меняет отношение <span class="nowrap"><icon e></icon> к <icon i></icon>,</span> и *время* пика больных, но *высота* этого пика и общее количество заболевших в конце концов оказываются такими же как и раньше.
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Почему так? Из-за *главной* идеи Эпидемиологического ликбеза:
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![](pics/r.png)
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Сокращение от "Reproduction number" ("Индекс репродукции"). Это *среднее* число людей, которых <icon i></icon> заражает перед тем как выздоровеет (или умрёт).
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![](pics/r2.png)
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**R** меняется по ходу вспышки из-за приобретаемого иммунитета и вводимых ограничений.
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**R<sub>0</sub>** -- это значение R *в начале вспышки, до иммунитета или ограничений*. R<sub>0</sub> лучше показывает силу вируса, но по-прежнему меняется от места к месту. К примеру R<sub>0</sub> куда выше в густонаселённых городах по сравнению с сельской местностью.
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(Многие новостные статьи -- и даже научные работы! -- путают между собой R и R<sub>0</sub>. Научная терминология не всегда удачна.)
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R<sub>0</sub> для сезонных гриппов обычно колеблется в районе 1.28[^r0_flu]. Это значит, что в *начале* вспышки гриппа каждый <icon i></icon> заражает *в среднем* 1.28 человека. (Если вам представляется странным, что это число не целое, вспомните, что у "средней" матери 2.4 ребёнка. Это не значит, что где-то вокруг неё бегают половинки детей.)
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[^r0_flu]: “The median R value for seasonal influenza was 1.28 (IQR: 1.19–1.37)” [Biggerstaff, M., Cauchemez, S., Reed, C. et al.](https://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-14-480)
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По оценкам, R<sub>0</sub> для COVID-19 составляет около 2.2,[^r0_covid] хотя одно из *незавершённых* исследований даёт оценку в 5.7(!) для Ухани.[^r0_wuhan]
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[^r0_covid]: “We estimated the basic reproduction number R0 of 2019-nCoV to be around 2.2 (90% high density interval: 1.4–3.8)” [Riou J, Althaus CL.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001239/)
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[^r0_wuhan]: “we calculated a median R0 value of 5.7 (95% CI 3.8–8.9)” [Sanche S, Lin YT, Xu C, Romero-Severson E, Hengartner N, Ke R.](https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article)
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В наших симуляциях -- *в начале и в среднем* -- <icon i></icon> заражает другого раз в 4 дня в течение 10 дней. "4 дня" укладываются в "10 дней" два с половиной раза. Это означает -- *в начале и в среднем* -- что каждый <icon i></icon> заразил 2.5 других. Следовательно, R<sub>0</sub> = 2.5. (оговорки:[^r0_caveats_sim])
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[^r0_caveats_sim]: В предположении что человек одинаково заразен на протяжении всей болезни. Опять же, мы упрощаем для наглядности.
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**Поиграйте с калькулятором R<sub>0</sub>, чтобы увидеть, как R<sub>0</sub> зависит от времени выздоровления и интервала между заражениями:**
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<div class="sim">
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<iframe src="sim?stage=epi-6a&format=calc" width="285" height="255"></iframe>
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</div>
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Но учтите, что чем меньше у нас <span class="nowrap"><icon s></icon>,</span> тем *медленнее* <span class="nowrap"><icon s></icon></span> становятся <span class="nowrap"><icon i></icon>.</span> *Текущий* индекс репродукции (R) зависит не только от *базового* (R<sub>0</sub>), но *ещё* и от того, сколько людей больше не <icon s></icon> Уязвимы (скажем, потому что они выздоровели и приобрели иммунитет.)
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<div class="sim">
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<iframe src="sim?stage=epi-6b&format=calc" width="285" height="390"></iframe>
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</div>
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Как только иммунитет приобретают достаточно много людей, R < 1, то есть распространение удалось остановить. Это называется **Коллективный иммунитет**. Для гриппов коллективного иммунитета добиваются при помощи *вакцинации*. Ни в коем случае не стоит пытаться достичь "естественного коллективный иммунитета", просто позволяя людям заражаться (Не потому, о чём вы подумали! Мы объясним это позднее).
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Теперь давайте поиграем с моделью SEOR снова, следя за R<sub>0</sub> и R со временем, и посмотрим на порог коллективного иммунитета:
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<div class="sim">
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<iframe src="sim?stage=epi-7" width="800" height="540"></iframe>
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</div>
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**Обратите внимание: болезнь не прекратила распространяться после достижения коллективного иммунитета, а намного переплюнула эту точку!** И она пересекает порог *ровно* в момент, когда число больных достигает пика. (Это происходит при любых настройках -- можете сами попробовать!)
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Это случается из-за того, что как только <span class="nowrap">не-<icon s ></icon></span> становится больше порога коллективного иммунитета, мы приходим в R < 1. А когда R < 1, число больных перестаёт расти: случается пик.
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**Важнейший момент, который стоит вынести из этой статьи, представлен на диаграмме ниже** -- она весьма запутана, так что уделите достаточно внимания, чтобы полностью осознать её смысл:
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![](pics/r3.png)
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**Это значит, что нам НЕ обязательно отлавливать всех или почти всех больных, чтобы остановить COVID-19!**
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|
||
Это парадоксально. COVID-19 очень заразный, но чтобы его остановить, нам достаточно "только" предотвратить принятыми мерами 60% заражений. 60%?! If that was a school grade, that's a D-. Но если R<sub>0</sub> = 2.5, то 61% даст нам R = 0.975, то есть R < 1 и распространение остановлено! (Точная формула:[^exact_formula])
|
||
|
||
[^exact_formula]: Вспомним, что R = R<sub>0</sub> * (долю до сих пор возможных при всех принятых мерах и иммунитете заражений). А доля возможных заражений -- это 1 - доля *предотвращённых* заражений.
|
||
|
||
|
||
Поэтому чтобы добиться R < 1, надо добиться R<sub>0</sub> * ВозможныеЗаражения < 1.
|
||
|
||
Следовательно, ВозможныеЗаражения < 1/R_0
|
||
|
||
Следовательно, 1 - ПредотвращённыеЗаражения < 1/R<sub>0</sub>
|
||
|
||
|
||
Следовательно, ПредотвращённыеЗаражения > 1 - 1/R<sub>0</sub>
|
||
|
||
|
||
Следовательно, достаточно остановить больше, чем **1 - 1/R<sub>0</sub>** всех заражений, чтобы получить R < 1 и сдержать распространение!
|
||
|
||
![](pics/r4.png)
|
||
|
||
(Если вы думаете, что R_0 или другие числа в нашей симуляции слишком низкие или высокие, то здорово, что вы подвергаете сомнению наши предположения! В конце этой статьи будет "режим песочницы, в котором вы сможете подставить *свои* числа и просимулировать, что случится.)
|
||
|
||
*Каждая* принятая мера, про которую вы слышали: мытьё рук, самоизоляция, соблюдение физической дистанции, карантин, отслеживание контактов, закрытие границ, ограничение передвижения, маски и даже "Коллективный иммунитет" -- они *все* добиваются одного и того же:
|
||
|
||
R < 1.
|
||
|
||
Теперь давайте используем наш "эпидемический симулятор полёта", чтобы выяснить: как мы можем достичь R < 1 **сохранив наше психическое здоровье *и* финансовое состояние?**
|
||
|
||
Приготовьтесь к аварийной посадке...
|
||
|
||
<div class="section chapter">
|
||
<div>
|
||
<img src="banners/curve.png" height=480 style="position: absolute;"/>
|
||
<div>The Next Few Months</div>
|
||
</div>
|
||
</div>
|
||
|
||
...could have been worse. Here's a parallel universe we avoided:
|
||
|
||
###Scenario 0: Do Absolutely Nothing
|
||
|
||
Around 1 in 20 people infected with COVID-19 need to go to an ICU (Intensive Care Unit).[^icu_covid] In a rich country like the USA, there's 1 ICU bed per 3400 people.[^icu_us] Therefore, the USA can handle 20 out of 3400 people being *simultaneously* infected – or, 0.6% of the population.
|
||
|
||
[^icu_covid]: ["Percentage of COVID-19 cases in the United States from February 12 to March 16, 2020 that required intensive care unit (ICU) admission, by age group"](https://www.statista.com/statistics/1105420/covid-icu-admission-rates-us-by-age-group/). Between 4.9% to 11.5% of *all* COVID-19 cases required ICU. Generously picking the lower range, that's 5% or 1 in 20. Note that this total is specific to the US's age structure, and will be higher in countries with older populations, lower in countries with younger populations.
|
||
|
||
[^icu_us]: “Number of ICU beds = 96,596”. From [the Society of Critical Care Medicine](https://sccm.org/Blog/March-2020/United-States-Resource-Availability-for-COVID-19) USA Population was 328,200,000 in 2019. 96,596 out of 328,200,000 = roughly 1 in 3400.
|
||
|
||
Even if we *more than tripled* that capacity to 2%, here's what would've happened *if we did absolutely nothing:*
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-1&format=lines" width="800" height="540"></iframe>
|
||
</div>
|
||
|
||
Not good.
|
||
|
||
That's what [the March 16 Imperial College report](http://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-9-impact-of-npis-on-covid-19/) found: do nothing, and we run out of ICUs, with more than 80% of the population getting infected.
|
||
(remember: total cases *overshoots* herd immunity)
|
||
|
||
Even if only 0.5% of infected die – a generous assumption when there's no more ICUs – in a large country like the US, with 300 million people, 0.5% of 80% of 300 million = still 1.2 million dead... *IF we did nothing.*
|
||
|
||
(Lots of news & social media reported "80% will be infected" *without* "IF WE DO NOTHING". Fear was channelled into clicks, not understanding. *Sigh.*)
|
||
|
||
###Scenario 1: Flatten The Curve / Herd Immunity
|
||
|
||
The "Flatten The Curve" plan was touted by every public health organization, while the United Kingdom's original "herd immunity" plan was universally booed. They were *the same plan.* The UK just communicated theirs poorly.[^yong]
|
||
|
||
[^yong]: “He says that the actual goal is the same as that of other countries: flatten the curve by staggering the onset of infections. As a consequence, the nation may achieve herd immunity; it’s a side effect, not an aim. [...] The government’s actual coronavirus action plan, available online, doesn’t mention herd immunity at all.”
|
||
|
||
From a [The Atlantic article by Ed Yong](https://www.theatlantic.com/health/archive/2020/03/coronavirus-pandemic-herd-immunity-uk-boris-johnson/608065/)
|
||
|
||
Both plans, though, had a literally fatal flaw.
|
||
|
||
First, let's look at the two main ways to "flatten the curve": handwashing & physical distancing.
|
||
|
||
Increased handwashing cuts flus & colds in high-income countries by ~25%[^handwashing], while the city-wide lockdown in London cut close contacts by ~70%[^london]. So, let's assume handwashing can reduce R by *up to* 25%, and distancing can reduce R by *up to* 70%:
|
||
|
||
[^handwashing]: “All eight eligible studies reported that handwashing lowered risks of respiratory infection, with risk reductions ranging from 6% to 44% [pooled value 24% (95% CI 6–40%)].” We rounded up the pooled value to 25% in these simulations for simplicity. [Rabie, T. and Curtis, V.](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3156.2006.01568.x) Note: as this meta-analysis points out, the quality of studies for handwashing (at least in high-income countries) are awful.
|
||
|
||
[^london]: “We found a 73% reduction in the average daily number of contacts observed per participant. This would be sufficient to reduce R0 from a value from 2.6 before the lockdown to 0.62 (0.37 - 0.89) during the lockdown”. We rounded it down to 70% in these simulations for simplicity. [Jarvis and Zandvoort et al](https://cmmid.github.io/topics/covid19/comix-impact-of-physical-distance-measures-on-transmission-in-the-UK.html)
|
||
|
||
**Play with this calculator to see how % of <span class="nowrap">non-<icon s></icon>,</span> handwashing, and distancing reduce R:** (this calculator visualizes their *relative* effects, which is why increasing one *looks* like it decreases the effect of the others.[^log_caveat])
|
||
|
||
[^log_caveat]: This distortion would go away if we plotted R on a logarithmic scale... but then we'd have to explain *logarithmic scales.*
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-2a&format=calc" width="285" height="260"></iframe>
|
||
</div>
|
||
|
||
Now, let's simulate what happens to a COVID-19 epidemic if, starting March 2020, we had increased handwashing but only *mild* physical distancing – so that R is lower, but still above 1:
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-2&format=lines" width="800" height="540"></iframe>
|
||
</div>
|
||
|
||
Three notes:
|
||
|
||
1. This *reduces* total cases! **Even if you don't get R < 1, reducing R still saves lives, by reducing the 'overshoot' above herd immunity.** Lots of folks think "Flatten The Curve" spreads out cases without reducing the total. This is impossible in *any* Epidemiology 101 model. But because the news reported "80%+ will be infected" as inevitable, folks thought total cases will be the same no matter what. *Sigh.*
|
||
|
||
2. Due to the extra interventions, current cases peak *before* herd immunity is reached. In fact, in this simulation, total cases only overshoots *a tiny bit* above herd immunity – the UK's plan! At that point, R < 1, you can let go of all other interventions, and COVID-19 stays contained! Well, except for one problem...
|
||
|
||
3. You still run out of ICUs. For several months. (and remember, we *already* tripled ICUs for these simulations)
|
||
|
||
That was the other finding of the March 16 Imperial College report, which convinced the UK to abandon its original plan. Any attempt at **mitigation** (reduce R, but R > 1) will fail. The only way out is **suppression** (reduce R so that R < 1).
|
||
|
||
![](pics/mitigation_vs_suppression.png)
|
||
|
||
That is, don't merely "flatten" the curve, *crush* the curve. For example, with a...
|
||
|
||
###Scenario 2: Months-Long Lockdown
|
||
|
||
Let's see what happens if we *crush* the curve with a 5-month lockdown, reduce <icon i></icon> to nearly nothing, then finally – *finally* – return to normal life:
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-3&format=lines" width="800" height="540"></iframe>
|
||
</div>
|
||
|
||
Oh.
|
||
|
||
This is the "second wave" everyone's talking about. As soon as we remove the lockdown, we get R > 1 again. So, a single leftover <icon i></icon> (or imported <span class="nowrap"><icon i></icon>)</span> can cause a spike in cases that's almost as bad as if we'd done Scenario 0: Absolutely Nothing.
|
||
|
||
**A lockdown isn't a cure, it's just a restart.**
|
||
|
||
So, what, do we just lockdown again & again?
|
||
|
||
###Scenario 3: Intermittent Lockdown
|
||
|
||
This solution was first suggested by the March 16 Imperial College report, and later again by a Harvard paper.[^lockdown_harvard]
|
||
|
||
[^lockdown_harvard]: “Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022.” [Kissler and Tedijanto et al](https://science.sciencemag.org/content/early/2020/04/14/science.abb5793)
|
||
|
||
**Here's a simulation:** (After playing the "recorded scenario", you can try simulating your *own* lockdown schedule, by changing the sliders *while* the simulation is running! Remember you can pause & continue the sim, and change the simulation speed)
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-4&format=lines" width="800" height="540"></iframe>
|
||
</div>
|
||
|
||
This *would* keep cases below ICU capacity! And it's *much* better than an 18-month lockdown until a vaccine is available. We just need to... shut down for a few months, open up for a few months, and repeat until a vaccine is available. (And if there's no vaccine, repeat until herd immunity is reached... in 2022.)
|
||
|
||
Look, it's nice to draw a line saying "ICU capacity", but there's lots of important things we *can't* simulate here. Like:
|
||
|
||
**Mental Health:** Loneliness is one of the biggest risk factors for depression, anxiety, and suicide. And it's as associated with an early death as smoking 15 cigarettes a day.[^loneliness]
|
||
|
||
[^loneliness]: See [Figure 6 from Holt-Lunstad & Smith 2010](https://journals.sagepub.com/doi/abs/10.1177/1745691614568352). Of course, big disclaimer that they found a *correlation*. But unless you want to try randomly assigning people to be lonely for life, observational evidence is all you're gonna get.
|
||
|
||
**Financial Health:** "What about the economy" sounds like you care more about dollars than lives, but "the economy" isn't just stocks: it's people's ability to provide food & shelter for their loved ones, to invest in their kids' futures, and enjoy arts, foods, videogames – the stuff that makes life worth living. And besides, poverty *itself* has horrible impacts on mental and physical health.
|
||
|
||
Not saying we *shouldn't* lock down again! We'll look at "circuit breaker" lockdowns later. Still, it's not ideal.
|
||
|
||
But wait... haven't Taiwan and South Korea *already* contained COVID-19? For 4 whole months, *without* long-term lockdowns?
|
||
|
||
How?
|
||
|
||
###Scenario 4: Test, Trace, Isolate
|
||
|
||
*"Sure, we \*could've\* done what Taiwan & South Korea did at the start, but it's too late now. We missed the start."*
|
||
|
||
But that's exactly it! “A lockdown isn't a cure, it's just a restart”... **and a fresh start is what we need.**
|
||
|
||
To understand how Taiwan & South Korea contained COVID-19, we need to understand the exact timeline of a typical COVID-19 infection[^timeline]:
|
||
|
||
[^timeline]: **3 days on average to infectiousness:** “Assuming an incubation period distribution of mean 5.2 days from a separate study of early COVID-19 cases, we inferred that infectiousness started from 2.3 days (95% CI, 0.8–3.0 days) before symptom onset” (translation: Assuming symptoms start at 5 days, infectiousness starts 2 days before = Infectiousness starts at 3 days) [He, X., Lau, E.H.Y., Wu, P. et al.](https://www.nature.com/articles/s41591-020-0869-5)
|
||
|
||
**4 days on average to infecting someone else:** “The mean [serial] interval was 3.96 days (95% CI 3.53–4.39 days)” [Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Ancel Meyers L](https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article)
|
||
|
||
**5 days on average to feeling symptoms:** “The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days)” [Lauer SA, Grantz KH, Bi Q, et al](https://annals.org/AIM/FULLARTICLE/2762808/INCUBATION-PERIOD-CORONAVIRUS-DISEASE-2019-COVID-19-FROM-PUBLICLY-REPORTED)
|
||
|
||
![](pics/timeline1.png)
|
||
|
||
If cases only self-isolate when they know they're sick (that is, they feel symptoms), the virus can still spread:
|
||
|
||
![](pics/timeline2.png)
|
||
|
||
And in fact, 44% of all transmissions are like this: *pre*-symptomatic! [^pre_symp]
|
||
|
||
[^pre_symp]: “We estimated that 44% (95% confidence interval, 25–69%) of secondary cases were infected during the index cases’ presymptomatic stage” [He, X., Lau, E.H.Y., Wu, P. et al](https://www.nature.com/articles/s41591-020-0869-5)
|
||
|
||
But, if we find *and quarantine* a symptomatic case's recent close contacts... we stop the spread, by staying one step ahead!
|
||
|
||
![](pics/timeline3.png)
|
||
|
||
This is called **contact tracing**. It's an old idea, was used at an unprecedented scale to contain Ebola[^ebola], and now it's core part of how Taiwan & South Korea are containing COVID-19!
|
||
|
||
[^ebola]: “Contact tracing was a critical intervention in Liberia and represented one of the largest contact tracing efforts during an epidemic in history.” [Swanson KC, Altare C, Wesseh CS, et al.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6152989/)
|
||
|
||
(It also lets us use our limited tests more efficiently, to find pre-symptomatic <span class="nowrap"><icon i></icon>s</span> without needing to test almost everyone.)
|
||
|
||
Traditionally, contacts are found with in-person interviews, but those *alone* are too slow for COVID-19's ~48 hour window. That's why contact tracers need help, and be supported by – *NOT* replaced by – contact tracing apps.
|
||
|
||
(This idea didn't come from "techies": using an app to fight COVID-19 was first proposed by [a team of Oxford epidemiologists](https://science.sciencemag.org/content/early/2020/04/09/science.abb6936).)
|
||
|
||
Wait, apps that trace who you've been in contact with?... Does that mean giving up privacy, giving in to Big Brother?
|
||
|
||
Heck no! **[DP-3T](https://github.com/DP-3T/documents#decentralized-privacy-preserving-proximity-tracing)**, a team of epidemiologists & cryptographers (including one of us, Marcel Salathé) is *already* making a contact tracing app – with code available to the public – that reveals **no info about your identity, location, who your contacts are, or even *how many contacts* you've had.**
|
||
|
||
Here's how it works:
|
||
|
||
![](pics/dp3t.png)
|
||
|
||
([Here's the full comic](https://ncase.me/contact-tracing/). Details about "pranking"/false positives/etc in footnote:[^dp3t_details])
|
||
|
||
[^dp3t_details]: To prevent "pranking" (people falsely claiming to be infected), the DP-3T Protocol requires that the hospital first give you a One-Time Passcode that lets you upload your messages.
|
||
|
||
False positives are a problem in both manual & digital contact tracing. Still, we can reduce false positives in 2 ways: 1) By notifying Bobs only if they heard, say, 30+ min worth of messages, not just one message in passing. And 2) If the app *does* think Bob's been exposed, it can refer Bob to a *manual* contact tracer, for an in-depth follow-up interview.
|
||
|
||
For other issues like data bandwidth, source integrity, and other security issues, check out [the open-source DP-3T whitepapers!](https://github.com/DP-3T/documents#decentralized-privacy-preserving-proximity-tracing)
|
||
|
||
Along with similar teams like TCN Protocol[^tcn] and MIT PACT[^pact], they've inspired Apple & Google to bake privacy-first contact tracing directly into Android/iOS.[^gapple] (Don't trust Google/Apple? Good! The beauty of this system is it doesn't *need* trust!) Soon, your local public health agency may ask you to download an app. If it's privacy-first with publicly-available code, please do!
|
||
|
||
[^tcn]: [Temporary Contact Numbers, a decentralized, privacy-first contact tracing protocol](https://github.com/TCNCoalition/TCN#tcn-protocol)
|
||
|
||
[^pact]: [PACT: Private Automated Contact Tracing](https://pact.mit.edu/)
|
||
|
||
[^gapple]: [Apple and Google partner on COVID-19 contact tracing technology ](https://www.apple.com/ca/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/). Note they're not making the apps *themselves*, just creating the systems that will *support* those apps.
|
||
|
||
But what about folks without smartphones? Or infections through doorknobs? Or "true" asymptomatic cases? Contact tracing apps can't catch all transmissions... *and that's okay!* We don't need to catch *all* transmissions, just 60%+ to get R < 1.
|
||
|
||
(Footnote rant about the confusion between pre-symptomatic vs "true" asymptomatic – "true" asymptomatics are rare:[^rant])
|
||
|
||
[^rant]: Lots of news reports – and honestly, many research papers – did not distinguish between "cases who showed no symptoms when we tested them" (pre-symptomatic) and "cases who showed no symptoms *ever*" (true asymptomatic). The only way you could tell the difference is by following up with cases later.
|
||
|
||
Which is what [this study](https://wwwnc.cdc.gov/eid/article/26/8/20-1274_article) did. (Disclaimer: "Early release articles are not considered as final versions.") In a call center in South Korea that had a COVID-19 outbreak, "only 4 (1.9%) remained asymptomatic within 14 days of quarantine, and none of their household contacts acquired secondary infections."
|
||
|
||
So that means "true asymptomatics" are rare, and catching the disease from a true asymptomatic may be even rarer!
|
||
|
||
Isolating *symptomatic* cases would reduce R by up to 40%, and quarantining their *pre/a-symptomatic* contacts would reduce R by up to 50%[^oxford]:
|
||
|
||
[^oxford]: From the same Oxford study that first recommended apps to fight COVID-19: [Luca Ferretti & Chris Wymant et al](https://science.sciencemag.org/content/early/2020/04/09/science.abb6936/tab-figures-data) See Figure 2. Assuming R<sub>0</sub> = 2.0, they found that:
|
||
|
||
* Symptomatics contribute R = 0.8 (40%)
|
||
* Pre-symptomatics contribute R = 0.9 (45%)
|
||
* Asymptomatics contribute R = 0.1 (5%, though their model has uncertainty and it could be much lower)
|
||
* Environmental stuff like doorknobs contribute R = 0.2 (10%)
|
||
|
||
And add up the pre- & a-symptomatic contacts (45% + 5%) and you get 50% of R!
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-4a&format=calc" width="285" height="340"></iframe>
|
||
</div>
|
||
|
||
Thus, even without 100% contact quarantining, we can get R < 1 *without a lockdown!* Much better for our mental & financial health. (As for the cost to folks who have to self-isolate/quarantine, *governments should support them* – pay for the tests, job protection, subsidized paid leave, etc. Still way cheaper than intermittent lockdown.)
|
||
|
||
We then keep R < 1 until we have a vaccine, which turns susceptible <span class="nowrap"><icon s></icon>s</span> into immune <span class="nowrap"><icon r></icon>s.</span> Herd immunity, the *right* way:
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-4b&format=calc" width="285" height="230"></iframe>
|
||
</div>
|
||
|
||
(Note: this calculator pretends the vaccines are 100% effective. Just remember that in reality, you'd have to compensate by vaccinating *more* than "herd immunity", to *actually* get herd immunity)
|
||
|
||
Okay, enough talk. Here's a simulation of:
|
||
|
||
1. A few-month lockdown, until we can...
|
||
2. Switch to "Test, Trace, Isolate" until we can...
|
||
3. Vaccinate enough people, which means...
|
||
4. We win.
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-5&format=lines" width="800" height="540"></iframe>
|
||
</div>
|
||
|
||
So that's it! That's how we make an emergency landing on this plane.
|
||
|
||
That's how we beat COVID-19.
|
||
|
||
...
|
||
|
||
But what if things *still* go wrong? Things have gone horribly wrong already. That's fear, and that's good! Fear gives us energy to create *backup plans*.
|
||
|
||
The pessimist invents the parachute.
|
||
|
||
###Scenario 4+: Masks For All, Summer, Circuit Breakers
|
||
|
||
What if R<sub>0</sub> is way higher than we thought, and the above interventions, even with mild distancing, *still* aren't enough to get R < 1?
|
||
|
||
Remember, even if we can't get R < 1, reducing R still reduces the "overshoot" in total cases, thus saving lives. But still, R < 1 is the ideal, so here's a few other ways to reduce R:
|
||
|
||
**Masks For All:**
|
||
|
||
*"Wait,"* you might ask, *"I thought face masks don't stop you from getting sick?"*
|
||
|
||
You're right. Masks don't stop you from getting sick[^incoming]... they stop you from getting *others* sick.
|
||
|
||
[^incoming]: “None of these surgical masks exhibited adequate filter performance and facial fit characteristics to be considered respiratory protection devices.” [Tara Oberg & Lisa M. Brosseau](https://www.sciencedirect.com/science/article/pii/S0196655307007742)
|
||
|
||
[^outgoing]: “The overall 3.4 fold reduction [70% reduction] in aerosol copy numbers we observed combined with a nearly complete elimination of large droplet spray demonstrated by Johnson et al. suggests that surgical masks worn by infected persons could have a clinically significant impact on transmission.” [Milton DK, Fabian MP, Cowling BJ, Grantham ML, McDevitt JJ](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591312/)
|
||
|
||
[^homemade]: [Davies, A., Thompson, K., Giri, K., Kafatos, G., Walker, J., & Bennett, A](https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/testing-the-efficacy-of-homemade-masks-would-they-protect-in-an-influenza-pandemic/0921A05A69A9419C862FA2F35F819D55) See Table 1: a 100% cotton T-shirt has around 2/3 the filtration efficiency as a surgical mask, for the two bacterial aerosols they tested.
|
||
|
||
![](pics/masks.png)
|
||
|
||
To put a number on it: surgical masks *on the infectious person* reduce cold & flu viruses in aerosols by 70%.[^outgoing] Reducing transmissions by 70% would be as large an impact as a lockdown!
|
||
|
||
However, we don't know for sure the impact of masks on COVID-19 *specifically*. In science, one should only publish a finding if you're 95% sure of it. (...should.[^replication]) Masks, as of May 1st 2020, are less than "95% sure".
|
||
|
||
[^replication]: Any actual scientist who read that last sentence is probably laugh-crying right now. See: [p-hacking](https://en.wikipedia.org/wiki/Data_dredging), [the replication crisis](https://en.wikipedia.org/wiki/Replication_crisis))
|
||
|
||
However, pandemics are like poker. **Make bets only when you're 95% sure, and you'll lose everything at stake.** As a recent article on masks in the British Medical Journal notes,[^precautionary] we *have* to make cost/benefit analyses under uncertainty. Like so:
|
||
|
||
[^precautionary]: “It is time to apply the precautionary principle” [Trisha Greenhalgh et al \[PDF\]](https://www.bmj.com/content/bmj/369/bmj.m1435.full.pdf)
|
||
|
||
Cost: If homemade cloth masks (which are ~2/3 as effective as surgical masks[^homemade]), super cheap. If surgical masks, more expensive but still pretty cheap.
|
||
|
||
Benefit: Even if it's a 50–50 chance of surgical masks reducing transmission by 0% or 70%, the average "expected value" is still 35%, same as a half-lockdown! So let's guess-timate that surgical masks reduce R by up to 35%, discounted for our uncertainty. (Again, you can challenge our assumptions by turning the sliders up/down)
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-6a&format=calc" width="285" height="380"></iframe>
|
||
</div>
|
||
|
||
(other arguments for/against masks:[^mask_args])
|
||
|
||
[^mask_args]: **"We need to save supplies for hospitals."** *Absolutely agreed.* But that's more of an argument for increasing mask production, not rationing. In the meantime, we can make cloth masks.
|
||
|
||
**"They're hard to wear correctly."** It's also hard to wash your hands according to the WHO Guidelines – seriously, "Step 3) right palm over left dorsum"?! – but we still recommend handwashing, because imperfect is still better than nothing.
|
||
|
||
**"It'll make people more reckless with handwashing & social distancing."** Sure, and safety belts make people ignore stop signs, and flossing makes people eat rocks. But seriously, we'd argue the opposite: masks are a *constant physical reminder* to be careful – and in East Asia, masks are also a symbol of solidarity!
|
||
|
||
|
||
|
||
Masks *alone* won't get R < 1. But if handwashing & "Test, Trace, Isolate" only gets us to R = 1.10, having just 1/3 of people wear masks would tip that over to R < 1, virus contained!
|
||
|
||
**Summer:**
|
||
|
||
Okay, this isn't an "intervention" we can control, but it will help! Some news outlets report that summer won't do anything to COVID-19. They're half right: summer won't get R < 1, but it *will* reduce R.
|
||
|
||
For COVID-19, every extra 1° Celsius (1.8° Fahrenheit) makes R drop by 1.2%.[^heat] The summer-winter difference in New York City is 26°C (47°F),[^nyc_heat] so summer will make R drop by ~31%.
|
||
|
||
[^heat]: “One-degree Celsius increase in temperature [...] lower[s] R by 0.0225” and “The average R-value of these 100 cities is 1.83”. 0.0225 ÷ 1.83 = ~1.2%. [Wang, Jingyuan and Tang, Ke and Feng, Kai and Lv, Weifeng](https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3551767)
|
||
|
||
[^nyc_heat]: In 2019 at Central Park, hottest month (July) was 79.6°F, coldest month (Jan) was 32.5°F. Difference is 47.1°F, or ~26°C. [PDF from Weather.gov](https://www.weather.gov/media/okx/Climate/CentralPark/monthlyannualtemp.pdf)
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-6b&format=calc" width="285" height="220"></iframe>
|
||
</div>
|
||
|
||
Summer alone won't make R < 1, but if we have limited resources, we can scale back some interventions in the summer – so we can scale them *higher* in the winter.
|
||
|
||
**A "Circuit Breaker" Lockdown:**
|
||
|
||
And if all that *still* isn't enough to get R < 1... we can do another lockdown.
|
||
|
||
But we wouldn't have to be 2-months-closed / 1-month-open over & over! Because R is reduced, we'd only need one or two more "circuit breaker" lockdowns before a vaccine is available. (Singapore had to do this recently, "despite" having controlled COVID-19 for 4 months. That's not failure: this *is* what success takes.)
|
||
|
||
Here's a simulation of a "lazy case" scenario:
|
||
|
||
1. Lockdown, then
|
||
2. A moderate amount of hygiene & "Test, Trace, Isolate", with a mild amount of "Masks For All", then...
|
||
3. One more "circuit breaker" lockdown before a vaccine's found.
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=int-7&format=lines&height=620" width="800" height="620"></iframe>
|
||
</div>
|
||
|
||
Not to mention all the *other* interventions we could do, to further push R down:
|
||
|
||
* Travel restrictions/quarantines
|
||
* Temperature checks at malls & schools
|
||
* Deep-cleaning public spaces
|
||
* [Replacing hand-shaking with foot-bumping](https://twitter.com/V_actually/status/1233785527788285953)
|
||
* And all else human ingenuity shall bring
|
||
|
||
<p>. . .</p>
|
||
|
||
We hope these plans give you hope.
|
||
|
||
**Even under a pessimistic scenario, it *is* possible to beat COVID-19, while protecting our mental and financial health.** Use the lockdown as a "reset button", keep R < 1 with case isolation + privacy-protecting contact tracing + at *least* cloth masks for all... and life can get back to a normal-ish!
|
||
|
||
Sure, you may have dried-out hands. But you'll get to invite a date out to a comics bookstore! You'll get to go out with friends to watch the latest Hollywood cash-grab. You'll get to people-watch at a library, taking joy in people going about the simple business of *being alive.*
|
||
|
||
Even under the worst-case scenario... life perseveres.
|
||
|
||
So now, let's plan for some *worse* worst-case scenarios. Water landing, get your life jacket, and please follow the lights to the emergency exits:
|
||
|
||
<div class="section chapter">
|
||
<div>
|
||
<img src="banners/curve.png" height=480 style="position: absolute;"/>
|
||
<div>The Next Few Years</div>
|
||
</div>
|
||
</div>
|
||
|
||
You get COVID-19, and recover. Or you get the COVID-19 vaccine. Either way, you're now immune...
|
||
|
||
...*for how long?*
|
||
|
||
* COVID-19 is most closely related to SARS, which gave its survivors 2 years of immunity.[^SARS_immunity]
|
||
* The coronaviruses that cause "the" common cold give you 8 months of immunity.[^cold_immunity]
|
||
* There's reports of folks recovering from COVID-19, then testing positive again, but it's unclear if these are false positives.[^unclear]
|
||
* One *not-yet-peer-reviewed* study on monkeys showed immunity to the COVID-19 coronavirus for at least 28 days.[^monkeys]
|
||
|
||
But for COVID-19 *in humans*, as of May 1st 2020, "how long" is the big unknown.
|
||
|
||
[^SARS_immunity]: “SARS-specific antibodies were maintained for an average of 2 years \[...\] Thus, SARS patients might be susceptible to reinfection ≥3 years after initial exposure.” [Wu LP, Wang NC, Chang YH, et al.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851497/) "Sadly" we'll never know how long SARS immunity would have really lasted, since we eradicated it so quickly.
|
||
|
||
[^cold_immunity]: “We found no significant difference between the probability of testing positive at least once and the probability of a recurrence for the beta-coronaviruses HKU1 and OC43 at 34 weeks after enrollment/first infection.” [Marta Galanti & Jeffrey Shaman (PDF)](http://www.columbia.edu/~jls106/galanti_shaman_ms_supp.pdf)
|
||
|
||
[^unclear]: “Once a person fights off a virus, viral particles tend to linger for some time. These cannot cause infections, but they can trigger a positive test.” [from STAT News by Andrew Joseph](https://www.statnews.com/2020/04/20/everything-we-know-about-coronavirus-immunity-and-antibodies-and-plenty-we-still-dont/)
|
||
|
||
[^monkeys]: From [Bao et al.](https://www.biorxiv.org/content/10.1101/2020.03.13.990226v1.abstract) *Disclaimer: This article is a preprint and has not been certified by peer review (yet).* Also, to emphasize: they only tested re-infection 28 days later.
|
||
|
||
For these simulations, let's say it's 1 year.
|
||
**Here's a simulation starting with 100% <span class="nowrap"><icon r></icon>**,</span> exponentially decaying into susceptible, no-immunity <span class="nowrap"><icon s></icon>s</span> after 1 year, on *average*, with variation:
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=yrs-1&format=lines&height=600" width="800" height="600"></iframe>
|
||
</div>
|
||
|
||
Return of the exponential decay!
|
||
|
||
This is the **SEIRS Model**. The final "S" stands for <icon s></icon> Susceptible, again.
|
||
|
||
![](pics/seirs.png)
|
||
|
||
Now, let's simulate a COVID-19 outbreak, over 10 years, with no interventions... *if immunity only lasts a year:*
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=yrs-2&format=lines&height=600" width="800" height="600"></iframe>
|
||
</div>
|
||
|
||
In previous simulations, we only had *one* ICU-overwhelming spike. Now, we have several, *and* <icon i></icon> cases come to a rest *permanently at* ICU capacity. (Which, remember, we *tripled* for these simulations)
|
||
|
||
R = 1, it's **endemic.**
|
||
|
||
Thankfully, because summer reduces R, it'll make the situation better:
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=yrs-3&format=lines&height=640" width="800" height="640"></iframe>
|
||
</div>
|
||
|
||
Oh.
|
||
|
||
Counterintuitively, summer makes the spikes worse *and* regular! This is because summer reduces new <span class="nowrap"><icon i></icon>s,</span> but that in turn reduces new immune <span class="nowrap"><icon r></icon>s.</span> Which means immunity plummets in the summer, *creating* large regular spikes in the winter.
|
||
|
||
Thankfully, the solution to this is pretty straightforward – just vaccinate people every fall/winter, like we do with flu shots:
|
||
|
||
**(After playing the recording, try simulating your own vaccination campaigns! Remember you can pause/continue the sim at any time)**
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=yrs-4&format=lines" width="800" height="540"></iframe>
|
||
</div>
|
||
|
||
But here's the scarier question:
|
||
|
||
What if there's no vaccine for *years*? Or *ever?*
|
||
|
||
**To be clear: this is unlikely.** Most epidemiologists expect a vaccine in 1 to 2 years. Sure, there's never been a vaccine for any of the other coronaviruses before, but that's because SARS was eradicated quickly, and "the" common cold wasn't worth the investment.
|
||
|
||
Still, infectious disease researchers have expressed worries: What if we can't make enough?[^vax_enough] What if we rush it, and it's not safe?[^vax_safe]
|
||
|
||
[^vax_enough]: “If a coronavirus vaccine arrives, can the world make enough?” [by Roxanne Khamsi, on Nature](https://www.nature.com/articles/d41586-020-01063-8)
|
||
|
||
[^vax_safe]: “Don’t rush to deploy COVID-19 vaccines and drugs without sufficient safety guarantees” [by Shibo Jiang, on Nature](https://www.nature.com/articles/d41586-020-00751-9)
|
||
|
||
Even in the nightmare "no-vaccine" scenario, we still have 3 ways out. From most to least terrible:
|
||
|
||
1) Do intermittent or loose R < 1 interventions, to reach "natural herd immunity". (Warning: this will result in many deaths & damaged lungs. *And* won't work if immunity doesn't last.)
|
||
|
||
2) Do the R < 1 interventions forever. Contact tracing & wearing masks just becomes a new norm in the post-COVID-19 world, like how STI tests & wearing condoms became a new norm in the post-HIV world.
|
||
|
||
3) Do the R < 1 interventions until we develop treatments that make COVID-19 way, way less likely to need critical care. (Which we should be doing *anyway!*) Reducing ICU use by 10x is the same as increasing our ICU capacity by 10x:
|
||
|
||
**Here's a simulation of *no* lasting immunity, *no* vaccine, and not even any interventions – just slowly increasing capacity to survive the long-term spikes:**
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=yrs-5&format=lines" width="800" height="540"></iframe>
|
||
</div>
|
||
|
||
Even under the *worst* worst-case scenario... life perseveres.
|
||
|
||
<p>. . .</p>
|
||
|
||
Maybe you'd like to challenge our assumptions, and try different R<sub>0</sub>'s or numbers. Or try simulating your *own* combination of intervention plans!
|
||
|
||
**Here's an (optional) Sandbox Mode, with *everything* available. (scroll to see all controls) Simulate & play around to your heart's content:**
|
||
|
||
<div class="sim">
|
||
<iframe src="sim?stage=SB&format=sb" width="800" height="540"></iframe>
|
||
</div>
|
||
|
||
This basic "epidemic flight simulator" has taught us so much. It's let us answer questions about the past few months, next few months, and next few years.
|
||
|
||
So finally, let's return to...
|
||
|
||
<div class="section chapter">
|
||
<div>
|
||
<img src="banners/curve.png" height=480 style="position: absolute;"/>
|
||
<div>Сейчас</div>
|
||
</div>
|
||
</div>
|
||
|
||
Самолет затонул. Мы забрались на спасательные шлюпки. Пришло время найти сушу.[^dry_land]
|
||
|
||
[^dry_land]: Метафора суши [от Marc Lipsitch & Yonatan Grad, на STAT News](https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/)
|
||
|
||
Команды эпидемиологов и политиков ([левых](https://www.americanprogress.org/issues/healthcare/news/2020/04/03/482613/national-state-plan-end-coronavirus-crisis/), [правых](https://www.aei.org/research-products/report/national-coronavirus-response-a-road-map-to-reopening/ ), и [многопартийных](https://ethics.harvard.edu/covid-roadmap)) пришли к общему мнению о том, как победить COVID-19, защищая при этом наши жизни *и* наши свободы.
|
||
|
||
Вот примерное представление (более или менее единодушное) о запасном плане:
|
||
|
||
![](pics/plan.png)
|
||
|
||
И что же это значит для НАС, прямо сейчас?
|
||
|
||
**Для всех:** Соблюдайте самоизоляцию, чтобы мы могли выйти из Фазы I как можно скорее. Продолжайте мыть руки. Делайте маски самостоятельно. В следующем месяце станет доступным приложение для *конфиденциального* отслеживания вашего списка контактов, воспользуйтесь им, чтобы избежать взаимодействия с зараженными. Будьте здоровы, физически и ментально! И скажите местной исполнительной власти, чтобы они подняли свой зад и...
|
||
|
||
**Для местной исполнительной власти:** Принимайте законы для поддержки людей, вынужденных самоизолироваться/сидеть на карантине. Призывайте пользоваться приложениями для конфиденциального отслеживания списков контактов. Направьте больше средств на создание приборов/вещей, которые помогут справиться с COVID-19...
|
||
|
||
**Для создателей различных средств, помогающих справиться с COVID-19:** Делайте тесты. Создавайте средства для очистки воздуха. Создавайте средства индивидуальной защиты для больниц. Не забывайте делать тесты. Создавайте маски. Создавайте приложения. Создавайте противовирусные, профилактические и другие средства, которые не являются вакцинами. Принимайте участие в создании вакцины. Делайте тесты. Делайте больше тестов. Делайте еще больше тестов. Вселяйте надежду.
|
||
|
||
Не преуменьшайте страх, дабы вселять надежду. Наш страх должен *объединиться* с нашей надеждой, как объединились изобретатели самолетов и парашютов. Подготовка к ужасному будущему - это то, как мы *создаем* многообещающее будущее.
|
||
|
||
Единственное, чего стоит бояться, - это мысль, что единственное, чего стоит бояться, это страх..
|