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State-level needs for social distancing and contact tracing to contain COVID-19 in the United States

Author

Listed:
  • Weihsueh A. Chiu

    (Texas A&M University)

  • Rebecca Fischer

    (Texas A&M University)

  • Martial L. Ndeffo-Mbah

    (Texas A&M University
    Texas A&M University)

Abstract

Starting in mid-May 2020, many US states began relaxing social-distancing measures that were put in place to mitigate the spread of COVID-19. To evaluate the impact of relaxation of restrictions on COVID-19 dynamics and control, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths. We used this model to evaluate the impact of social distancing, testing and contact tracing on the COVID-19 epidemic in each state. As of 22 July 2020, we found that only three states were on track to curtail their epidemic curve. Thirty-nine states and the District of Columbia may have to double their testing and/or tracing rates and/or rolling back reopening by 25%, while eight states require an even greater measure of combined testing, tracing and distancing. Increased testing and contact-tracing capacity is paramount for mitigating the recent large-scale increases in US cases and deaths.

Suggested Citation

  • Weihsueh A. Chiu & Rebecca Fischer & Martial L. Ndeffo-Mbah, 2020. "State-level needs for social distancing and contact tracing to contain COVID-19 in the United States," Nature Human Behaviour, Nature, vol. 4(10), pages 1080-1090, October.
    • Handle: RePEc:nat:nathum:v:4:y:2020:i:10:d:10.1038_s41562-020-00969-7
    DOI: 10.1038/s41562-020-00969-7
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    1. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
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    Cited by:

    1. Yuan, Hang & Long, Qinyi & Huang, Guanglv & Huang, Liqin & Luo, Siyang, 2022. "Different roles of interpersonal trust and institutional trust in COVID-19 pandemic control," Social Science & Medicine, Elsevier, vol. 293(C).
    2. Yong Ge & Xilin Wu & Wenbin Zhang & Xiaoli Wang & Die Zhang & Jianghao Wang & Haiyan Liu & Zhoupeng Ren & Nick W. Ruktanonchai & Corrine W. Ruktanonchai & Eimear Cleary & Yongcheng Yao & Amy Wesolowsk, 2023. "Effects of public-health measures for zeroing out different SARS-CoV-2 variants," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Jiarui Fan & Haifeng Du & Yang Wang & Xiaochen He, 2021. "The Effect of Local and Global Interventions on Epidemic Spreading," IJERPH, MDPI, vol. 18(23), pages 1-13, November.
    4. Lu Zhong & Mamadou Diagne & Qi Wang & Jianxi Gao, 2022. "Vaccination and three non-pharmaceutical interventions determine the dynamics of COVID-19 in the US," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    5. Behzad Vahedi & Morteza Karimzadeh & Hamidreza Zoraghein, 2021. "Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    6. Siqing Shan & Feng Zhao & Menghan Sun & Yinong Li & Yangzi Yang, 2022. "Suit the Remedy to the Case—The Effectiveness of COVID-19 Nonpharmaceutical Prevention and Control Policies Based on Individual Going-Out Behavior," IJERPH, MDPI, vol. 19(23), pages 1-18, December.
    7. Kai Yin & Anirban Mondal & Martial Ndeffo-Mbah & Paromita Banerjee & Qimin Huang & David Gurarie, 2022. "Bayesian Inference for COVID-19 Transmission Dynamics in India Using a Modified SEIR Model," Mathematics, MDPI, vol. 10(21), pages 1-18, October.

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