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title: Untitled

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<div class="section">
    <div>
    	<iframe id="splash" width="960" height="480" src="banners/splash.html"></iframe>
        <div style="top: 70px;font-size: 75px;font-weight: bold;">
        	Apa Yang Terjadi Selanjutnya?
       	</div>
		<div style="font-weight: 500;top: 140px;left: 10px;font-size: 29px;">
			Masa Depan COVID-19, Dijelaskan Dengan Simulasi Interaktif
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		<div style="font-weight: 100;top: 189px;left: 10px;font-size: 19px;line-height: 21px;">
			<b>
				🕐 Waktu baca/interaksi: 30 menit
				&nbsp;&middot;&nbsp;
			</b>
			by
			<a href="https://scholar.google.com/citations?user=_wHMGkUAAAAJ&amp;hl=en">Marcel Salathé</a>
			(epidemiologis)
			&
			<a href="https://ncase.me/">Nicky Case</a>
			(desain/kode)
		</div>
	</div>
</div>

"Satu-satu hal yang ditakuti adalah rasa takut itu sendiri" adalah saran yang bodoh.

Tentu, tak perlu menimbun tisu toilet - namun jika pembuat kebijakan takut dengan rasa takutnya sendiri, mereka meremehkan bahaya sebenarnya untuk menghindari "kepanikan massal". Takut itu bukan masalahnya, namun bagaimana kita *menyalurkan* rasa takut kita. Takut memberikan kita kekuatan untuk menghadapi bahaya saat ini, dan mempersiapkan diri untuk bahaya di masa depan.

Sejujurnya, kami (Marcel, epidemiologis + Nicky, desain/kode) khawatir. Kami yakin Anda juga! Itu mengapa kami menyalurkan rasa takut kami dengan membuat **simulasi interaktif**, sehingga *Anda* bisa menyalurkan rasa takut dengan memahami:

* **Beberapa Bulan yang Lalu** (epidemiologi 101, mode SEIR, R & R<sub>0</sub>)
* **Beberapa Bulan yang Akan Datang** (karantina, pelacakan kontak, masker)
* **Beberapa Tahun yang Akan Datang** (hilangnya imunitas? tidak ada vaksin?)

Panduan ini (diterbitkan pada 1 Mei 2020. klik catatan kaki ini!→[^timestamp]) dibuat untuk memberimu harapan *dan* rasa takut. Untuk melawan COVID-19 **sekaligus melindungi kesehatan mental dan finansial kita**, kita perlu optimisme untuk membuat berbagai rencana, dan rasa pesimis untuk membuat rencana cadangan. Seperti yang Gladys Bronwyn Stern pernah katakan, *“The optimist invents the airplane and the pessimist the parachute.”* yang berarti *"Seseorang yang optimis menciptakan pesawat terbang dan yang pesimis hanya menciptakan parasut"*

[^timestamp]: Catatan kaki ini akan berisi sumber, tautan, dan komentar tambahan. Sukai komentar ini!
    
    **Panduan ini diterbitkan pada 1 Mei 2020.** Banyak rincian yang akan kedaluarsa, namun kami yakin panduan ini akan melingkupi 95% dari kemungkinan masa depan, dan Pengenalan Epidemologi akan tetap bermanfaat selamanya.
    
Jadi, bersiaplah: kita akan mulai perjalanan dengan turbulensi.

<div class="section chapter">
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		<img src="banners/curve.png" height=480 style="position: absolute;"/>
        <div>Beberapa Bulan yang Lalu</div>
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</div>

Pilot menggunakan simulator penerbangan untuk mempelajari bagaimana agar pesawat tidak jatuh.

**Ahli epidemiologi menggunakan simulator epidemi untuk mempelajari bagaimana agar ras manusia tidak jatuh**

Jadi, mari kita buat "simulator penerbangan epidemi" yang sangat, *sangat* sederhana! Dalam simulasi ini, <icon i></icon> Orang yang Menginfeksi dapat mengubah <icon s></icon> Orang yang Rentan menjadi lebih banyak <icon i></icon> Orang yang Menginfeksi:

![](pics/spread.png)

Diperkirakan bahwa, *pada awal* penjangkitan COVID-19, virus melompat dari <icon i></icon> ke <icon s></icon> setiap 4 hari, *rata-rata*.[^serial_interval] (ingat, ada banyak variasinya)

[^serial_interval]: “Interval [serial] rata-rata adalah 3,96 hari (95% CI 3,53–4,39 hari)”. [Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Ancel Meyers L](https://wwwnc.cdc.gov/eid/article/26/6/20-0357_article) (Catatan: Artikel yang dirilis lebih awal tidak dianggap sebagai versi akhir)

Jika kita mensimulasikan "kasus menjadi ganda setiap 4 hari" *dan tidak ada lagi*, dalam sebuah populasi dimulai dengan hanya 0,001% <icon i></icon>, apa yang terjadi? 

**Klik "Mulai" untuk memainkan simulasi! Anda dapat memainkannya ulang nanti dengan pengaturan berbeda**
**Click "Start" to play the simulation! You can re-play it later with different settings:** (peringatan teknis: [^caveats])

[^caveats]: **Ingat: semua simulasi ini sudah sangat disederhanakan, untuk keperluan edukasi.**
    
    Satu penyederhanaan: Ketika Anda memberitahu simulasi ini "Infeksi 1 orang baru setiap X hari", ini sebenarnya meningkatkan jumlah dari yang Menginfeksi dengan 1/x setiap harinya. Hal yang sama untuk pengaturan masa depan dari simulasi ini - "Sembuh setiap X hari" sebenarnya mengurangi sejumlah orang Menginfeksi dalam 1/X setiap harinya.
    
    Hal ini *tentu* tidak sama persis, namun hampir mendekati, dan untuk keperluan edukasi ini tidak lebih samar daripada mengatur tingkat transmisi/pemulihan secara langsung.

<div class="sim">
		<iframe src="sim?stage=epi-1" width="800" height="540"></iframe>
</div>

Ini adalah **kurva pertumbuhan eksponensial.** Awalnya sedikit, kemudian meledak. Dari "Oh ini hanya flu kok" menjadi "Oh iya, flu tidak membuat *kuburan massal di kota-kota besar*". 

![](pics/exponential.png)

Tetapi, simulasi ini kurang tepat. Untungnya, pertumbuhan eksponensial tidak berjalan selamanya. Satu hal yang menghentikan virus untuk menyebar adalah jika orang-orang *sudah* terjangkiti virus:

![](pics/susceptibles.png)

Semakin banyak <icon i></icon> bermunculan, semakin cepat <icon s></icon> berubah menjadi <icon i></icon>, **namun semakin sedikit <icon s></icon>, semakin *lambat* <icon s></icon> berubah menjadi <icon i></icon>.**

Bagaimana ini mengubah pertumbuhan sebuah epidemi? Mari kita cari tahu:

<div class="sim">
		<iframe src="sim?stage=epi-2" width="800" height="540"></iframe>
</div>

Ini adalah **kurva S pertumbuhan logistik**. Awalnya kecil, meledak, kemudian kembali melambat.

Namun, simulasi ini *masih* keliru. Kita kehilangan fakta bahwa <icon i></icon> Orang yang Menginfeksi secara berkala akan berhenti menularkan, antara dengan menjadi 1) sembuh, 2) "sembuh" dengan kerusakan paru, atau 3) sekarat.

Untuk menyederhanakannya, kita anggap kalau semua <icon i></icon> orang yang Menginfeksi berubah menjadi <icon r></icon> Sembuh. (Cukup ingat bahwa kenyataannya, beberapa di antaranya meninggal dunia.) <icon r></icon> tidak dapat ditulari lagi, dan mari kita anggap - *untuk sekarang!* - bahwa mereka tetap imun sepanjang hidupnya.

Dengan COVID-19, diperkirakan Anda <icon i></icon> menjadi orang yang Menginfeksi dalam 10 hari, *rata-rata*.[^penularan] Ini berarti beberapa orang akan sembuh sebelum 10 hari, yang lainnya lebih dari itu. **Seperti ini lah tampilannya, dengan sebuah simulasi *dimulai* dengan 100% <icon i></icon>:**

[^penularan]: “Angka median periode komunikasi \[...\] adalah 9,5 hari.” [Hu, Z., Song, C., Xu, C. et al](https://link.springer.com/article/10.1007/s11427-020-1661-4) Ya, kita tahu "angka median" tidak sama dengan "rata-rata". Untuk keperluan edukasi sederhana, ini cukup mendekati.

<div class="sim">
		<iframe src="sim?stage=epi-3" width="800" height="540"></iframe>
</div>

Ini adalah **kurva kehilangan eksponensial**, lawan dari pertumbuhan eksponensial.

Sekarang, apa yang terjadi jika Anda mensimulasikan kurva S logistik pertumbuhan *dengan* kesembuhan?

![](pics/graphs_q.png)

Mari kita cari tahu.

<b style='color:#ff4040'>Kurva merah</b> adalah kasus *saat ini* <icon i></icon>,    
<b style='color:#999999'>Kurva abu</b> adalah kasus *total* (kasus saat ini + kasus sembuh <icon r></icon>),
dimulai dengan hanya 0,001% <icon i></icon>:

<div class="sim">
		<iframe src="sim?stage=epi-4" width="800" height="540"></iframe>
</div>

Dan *itulah* asal di mana kurva terkenal tersebut muncul! Ini bukan kurva lonceng, bahkan bukan kurva "log-normal". Kurva ini tidak ada namanya. Tetapi Anda sudah melihatnya berjuta-juta kali, dan memohon-mohon agar kurva menjadi rata.

Ini adalah **Model SIR**,[^sir]    
(<icon s></icon>**S**usceptible/Kelas Rentan <icon i></icon>**I**nfectious/Kelas Terinfeksi <icon r></icon>**R**ecovered/Kelas Sembuh)      
gagasan paling penting kedua di Pengenalan Epidemiologi:

[^sir]: Untuk penjelasan Model SIR yang lebih teknis, lihat [the Institute for Disease Modeling](https://www.idmod.org/docs/hiv/model-sir.html#) dan [Wikipedia](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model)

![](pics/sir.png)

**CATATAN: Simulasi yang menginformasikan kebijakan itu jauh, *jauh* lebih canggih daripada ini!** Namun model SIR masih dapat menjelaskan temuan umum, walaupun jika kehilangan nuansanya.

Sebenarnya, mari kita tambahkan satu nuansa lain: sebelum seorang <icon s></icon> berubah menjadi <icon i></icon>, pertama-tama mereka berubah menjadi <icon e></icon> orang yang Terekspos. Ini adalah ketika mereka memiliki virus di tubuhnya namun belum dapat mengopernya ke orang lain - *ter*infeksi namun belum dapat *meng*infeksi.

![](pics/seir.png)

(Varian ini disebut sebagai **model SEIR**[^seir], di mana "E" berarti <icon e></icon> "Kelas Terekspos". Mohon dicatat bahwa ini *bukan* arti umum dari "terekspos", ketika Anda mungkin atau tidak memiliki virus. Dalam pengertian teknis, "Terekspos" berarti Anda benar-benar memiliki virusnya. Terminologi sains memang buruk.)

[^seir]: Untuk penjelasan Model SEIR yang lebih teknis, lihat, lihat [the Institute for Disease Modeling](https://www.idmod.org/docs/hiv/model-seir.html) dan [Wikipedia](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model)

Untuk COVID-19, diperkirakan bahwa Anda menjadi <icon e></icon> orang yang terinfeksi namun belum menginfeksi dalam waktu 3 hari, *dalam rata-rata*.[^latent] Apa yang terjadi jika kita menambahkannya ke dalam simulasi?

[^latent]: “Dengan asumsi distribusi periode inkubasi rata-rata 5,2 hari dari studi terpisah kasus COVID-19 awal, kami menyimpulkan bahwa infeksi dimulai dari 2,3 hari (95% CI, 0,8-3,0 hari) sebelum timbulnya gejala” (translation: Dengan asumsi gejala dimulai pada 5 hari, infeksi dimulai 2 hari sebelumnya = Infeksi dimulai pada 3 hari) [He, X., Lau, E.H.Y., Wu, P. et al.](https://www.nature.com/articles/s41591-020-0869-5)

<b style='color:#ff4040'>Kurva Merah <b style='color:#FF9393'>+ Merah Muda</b></b> adalah kasus *saat ini* (Kelas Terinfeksi <icon i></icon> + Kelas Terekspos <icon e></icon>),    
<b style='color:#888'>Kurva Abu</b> adalah kasus *total* (saat ini + Kelas Sembuh <icon r></icon>):

<div class="sim">
		<iframe src="sim?stage=epi-5" width="800" height="540"></iframe>
</div>

Tidak banyak pengubahan! Seberapa lama Anda tetap menjadi <icon e></icon> Kelas Terekspos mengubah laju pengubahan dari <icon e></icon>-ke-<icon i></icon>, dan *ketika* kasus saat ini memuncak... tetapi *ketinggian* puncak, dan total kasus pada akhirnya, tetap sama.

Mengapa bisa begitu? Ini dikarenakan oleh gagasan *pertama* dan paling penting dalam Pengenalan Epidemiologi:

![](pics/r.png)

Kependekan dari "angka Reproduksi". Ini adalah angka *rata-rata* orang yang seorang <icon i></icon> infeksi *sebelum* mereka sembuh (atau meninggal dunia).

![](pics/r2.png)

**R** berubah selama wabah, sepanjang kita mendapatkan lebih banyak imunitas & intervensi.

**R<sub>0</sub>** (diucapkan sebagai R-nought) adalah nilai R *pada permulaan penyebaran wabah, sebelum imunitas atau intervensi*. R<sub>0</sub> secara lebih dekat merefleksikan kekuatan virus itu sendiri, namun masih dapat berubah dari lokasi ke lokasi. Sebagai contoh, R<sub>0</sub> di kota-kota berkepadatan tinggi lebih tinggi daripada area pedesaan berkepadatan rendah.

(Mayoritas artikel berita – dan bahkan beberapa artikel ilmiah! – kebingungan antara R dan R<sub>0</sub>. Sekali lagi, terminologi sains memang buruk.)

R<sub>0</sub> untuk flu musiman itu sekitar 1,28[^r0_flu]. Ini berarti, pada saat *permulaan* wabah flu, setiap <icon i></icon> menginfeksi 1,28 orang lainnya *rata-rata* (Jika ini terdengar aneh bahwa ini bukan angka bulat, ingat bahwa "rata-rata" seorang ibu memiliki 2,4 anak. Hal ini bukan berarti ada setengah anak di tengah masyarakat.)

[^r0_flu]: “Angka R median untuk influenza musiman adalah 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)

Nilai R<sub>0</sub> untuk COVID-19 diperkirakan sekitar 2,2 ,[^r0_covid] walaupun sebuah studi *yang belum difinalisasi* memperkirakan nilainya 5,7(!) di Wuhan.[^r0_wuhan]

[^r0_covid]: “Kami memperkirakan angka reproduksi dasar R0 dari 2019-nCoV berkisar 2,2 (90% interval berkepadatan tinggi: 1,4–3,8)” [Riou J, Althaus CL.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001239/)

[^r0_wuhan]: “kami menghitung nilai median R0 sebesar 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)

Dalam simulasi kami - *pada permulaan & rata-rata* – seorang <icon i></icon> menginfeksi orang lain setiap 4 hari, selama 10 hari. "4 hari" dalam "10 hari" menjadi dua-setengah kali. Ini berarti - *pada permulaan & rata-rata* - setiap <icon i></icon> menginfeksi 2,5 orang lainnya. Sehingga, nilai R<sub>0</sub> = 2,5. (caveats:[^r0_caveats_sim])

[^r0_caveats_sim]: Ini berpura-pura bahwa Anda sama-sama menular di seluruh "periode menular" Anda. Sekali lagi, penyederhanaan untuk tujuan pendidikan.

**Mainkan dengan kalkulator R<sub>0</sub> ini, untuk melihat bagaimana R<sub>0</sub> bergantung pada waktu penyembuhan dan waktu infeksi baru:**

<div class="sim">
		<iframe src="sim?stage=epi-6a&format=calc" width="285" height="255"></iframe>
</div>

Tapi ingat, semakin sedikit <icon s></icon> berada, semakin *melambat* <icon s></icon> berubah menjadi <icon i></icon>. Angka reproduksi *saat ini* (R) bergantung tidak hanya pada angka reproduksi *dasar* (R<sub>0</sub>), namun *juga* bergantung pada berapa banyak orang yang tidak lagi termasuk <icon s></icon> Kelas Rentan. (Contohnya, mereka yang sembuh & mendapatkan imunitas secara alami.)

<div class="sim">
		<iframe src="sim?stage=epi-6b&format=calc" width="285" height="390"></iframe>
</div>

Ketika cukup banyak orang yang memiliki kekebalan, R <1, dan virusnya terkandung! Ini disebut ***herd immunity*** atau **kekebalan kelompok**. Untuk flu, kekebalan kelompok dicapai *dengan vaksin*. Usaha untuk mencapai "kekebalan kelompok alami" dengan membiarkan orang terinfeksi adalah gagasan *mengerikan*. (Tapi tidak untuk alasan yang mungkin kau pikirkan! Kami akan menjelaskannya nanti.)

Sekarang, mari kita mainkan Model SEIR lagi, namun sekarang menampilkan nilai R<sub>0</sub>, R sepanjang waktu, dan ambang batas kekebalan kelompok:

<div class="sim">
		<iframe src="sim?stage=epi-7" width="800" height="540"></iframe>
</div>

**NOTE: Total cases *does not stop* at herd immunity, but overshoots it!** And it crosses the threshold *exactly* when current cases peak. (This happens no matter how you change the settings – try it for yourself!)

This is because when there are more non-<icon s></icon>s than the herd immunity threshold, you get R < 1. And when R < 1, new cases stop growing: a peak.

**If there's only one lesson you take away from this guide, here it is** – it's an extremely complex diagram so please take time to fully absorb it:

![](pics/r3.png)

**This means: we do NOT need to catch all transmissions, or even nearly all transmissions, to stop COVID-19!**

It's a paradox. COVID-19 is extremely contagious, yet to contain it, we "only" need to stop more than 60% of infections. 60%?! If that was a school grade, that's a D-. But if R<sub>0</sub> = 2.5, cutting that by 61% gives us R = 0.975, which is R < 1, virus is contained! (exact formula:[^exact_formula])

[^exact_formula]: Remember R = R<sub>0</sub> * the ratio of transmissions still allowed. Remember also that ratio of transmissions allowed = 1 - ratio of transmissions *stopped*.
    
    Therefore, to get R < 1, you need to get R<sub>0</sub> * TransmissionsAllowed < 1. 
    
    Therefore, TransmissionsAllowed < 1/R<sub>0</sub>
    
    Therefore, 1 - TransmissionsStopped < 1/R<sub>0</sub>
    
    Therefore, TransmissionsStopped > 1 - 1/R<sub>0</sub>
    
    Therefore, you need to stop more than **1 - 1/R<sub>0</sub>** of transmissions to get R < 1 and contain the virus!

![](pics/r4.png)

(If you think R<sub>0</sub> or the other numbers in our simulations are too low/high, that's good you're challenging our assumptions! There'll be a "Sandbox Mode" at the end of this guide, where you can plug in your *own* numbers, and simulate what happens.)

*Every* COVID-19 intervention you've heard of – handwashing, social/physical distancing, lockdowns, self-isolation, contact tracing & quarantining, face masks, even "herd immunity" – they're *all* doing the same thing:

Getting R < 1.

So now, let's use our "epidemic flight simulator" to figure this out: How can we get R < 1 in a way **that also protects our mental health *and* financial health?**

Brace yourselves for an emergency landing...

<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 non-<icon s></icon>, 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 <icon i></icon>) 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 <icon i></icon>s 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/))

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.

(Rant about the confusion about 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 <icon s></icon>s into immune <icon r></icon>s. 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 sick 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 (2.2° Fahrenheit) makes R drop by 1.2%.[^heat] The summer-winter difference in New York City is 15°C (60°F), so summer will make R drop by 18%.

[^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)

<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 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

. . .

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 contract 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% <icon r></icon>**, exponentially decaying into susceptible, no-immunity <icon s></icon>s 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 <icon i></icon>s, but that in turn reduces new immune <icon r></icon>s. 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.

. . .

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>The Now</div>
    </div>
</div>

Plane's sunk. We've scrambled onto the life rafts. It's time to find dry land.[^dry_land]

[^dry_land]: Dry land metaphor [from Marc Lipsitch & Yonatan Grad, on STAT News](https://www.statnews.com/2020/04/01/navigating-covid-19-pandemic/)

Teams of epidemiologists and policymakers ([left](https://www.americanprogress.org/issues/healthcare/news/2020/04/03/482613/national-state-plan-end-coronavirus-crisis/), [right](https://www.aei.org/research-products/report/national-coronavirus-response-a-road-map-to-reopening/ ), and [multi-partisan](https://ethics.harvard.edu/covid-roadmap)) have come to a consensus on how to beat COVID-19, while protecting our lives *and* liberties.

Here's the rough idea, with some (less-consensus) backup plans:

![](pics/plan.png)

So what does this mean for YOU, right now?

**For everyone:** Respect the lockdown so we can get out of Phase I asap. Keep washing those hands. Make your own masks. Download a *privacy-protecting* contact tracing app when those are available next month. Stay healthy, physically & mentally! And write your local policymaker to get off their butt and...

**For policymakers:** Make laws to support folks who have to self-isolate/quarantine. Hire more manual contact tracers, *supported* by privacy-protecting contact tracing apps. Direct more funds into the stuff we should be building, like...

**For builders:** Build tests. Build ventilators. Build personal protective equipment for hospitals. Build tests. Build masks. Build apps. Build antivirals, prophylactics, and other treatments that aren't vaccines. Build vaccines. Build tests. Build tests. Build tests. Build hope. 

Don't downplay fear to build up hope. Our fear should *team up* with our hope, like the inventors of airplanes & parachutes. Preparing for horrible futures is how we *create* a hopeful future.

The only thing to fear is the idea that the only thing to fear is fear itself.