Evaluating Quantitative Research in Global Health: Strengths, Weaknesses, and Opportunities for Improvement.

Submitted as part of coursework in my Master’s degree in Global Public Health

Evaluating Quantitative Research in Global Health: Strengths, Weaknesses, and Opportunities for Improvement.

Quantitative research is the collection and analysis of primarily numerical data in order to identify patterns, test hypotheses, and aspires to establish generalisable principles in an objective manner (Bryman, 2016). Quantitative approaches are associated with a positivist paradigm that asserts that phenomena can be measured objectively, understood and predicted through observable data (Park et al, 2020), and are typically applied in global health research through utilising structured methods such as surveys, questionnaires and experiments. The resultant data is then analysed using statistical methods to infer and establish correlations amongst the measured variables. Quantitative methods aim to provide precise, quantifiable results that may be generalised to wider contexts and repeated by other researchers (Merson et al, 2012). 


It is useful to explore quantitative research in contrast with qualitative. While quantitative research focuses on numerical data, qualitative research aims to understand and interpret social phenomena by using non-numerical data such as words, images, and understanding. This involves methods including interviews, focus groups, and observations, which attempt to enable researchers to see through others’ perspectives. Unlike quantitative research, qualitative research does not aim to generalise findings, but instead aims to provide detailed and rich insights into specific contexts.


Quantitative approaches tend to be deductive; beginning with a hypothesis that may be proven or disproven. Qualitative research in contrast is often inductive – allowing the researcher to explore rich sources of information and develop insights or theories. The epistemological distinction between the positivist, quantitative approach and the more sociological, phenomenological, qualitative approach describes the different methods and their aims, however, the two approaches are regarded by many researchers as incompatible means for knowledge construction (Teddlie & Tashakkori, 2003). Some academics, practitioners and policymakers see qualitative research as weaker in validity than quantitative approaches, though others in the field of global health argue that qualitative approaches should  be seen as an indicator reliable enough to begin effective health promotion interventions (Trautmann & Burrows, 1999). Increasingly, global health researchers combine both approaches in mixed methods research designs which allow them to utilise the benefits of each approach whilst mitigating their weaknesses (Merson et al, 2012).


In 2013, an Ebola Virus Disease (EVD) outbreak that began in Guinea spread quickly to the bordering countries of Liberia and Sierra Leone. On March 23, 2014 the WHO declared an outbreak of EVD and by July 2014 it was considered a global health crisis (McInnes, 2016). Poor surveillance systems and public health infrastructure contributed to the rapid spread of the disease. A year later, in March 2015, EVD cases in Sierra Leona continued to rise (Brooks, 2015), 


Researchers Park et al. conducted a comparative analysis of sequences from 673 patients in Sierra Leone between December 2014 and May 2015, along with genomes from 88 patients during the earlier phase of the epidemic. The aim was to discern the transmission patterns of the virus within the country and identify potential cases resulting from cross-border movements (Park et al, 2015). This quantitative research spanned seven months from initial genome sequencing to completed data analysis. It provided compelling evidence that EVD transmission was predominantly within the country rather than from outside Sierra Leone. The findings from this paper provided decision makers with a solid rationale for restricting movement to reduce the spread of EVD. 


However, it provided little insight into how those restrictions could be applied in a culturally sensitive and context-appropriate manner that would maximise compliance with movement restrictions. Over 9,430 new EVD cases in Sierra Leona were reported over the trial period from December 2014 to May 2015, causing around 6000 deaths, according to 2017 CDC data (CDC, 2017). The transmission of EVD is generally via bodily fluids such as blood, faeces and vomit, and death is usually a result of haemorrhaging through orifices and the skin itself, which renders the bodies of EVD victims highly infectious. Sierra Leonean funeral traditions include washing the corpse by hand before burial as well as touching the corpse during the ceremony (Richards et al., 2015). Knowing this, it was important to gain a contextual understanding of that behaviour, and how it might be changed in culturally and interpersonally sensitive ways, in order to promote safe burial practice that reduced transmission of EVD. A Rapid Qualitative Assessment using focus group discussions was conducted in 2014 to explore community attitudes, knowledge and practices regarding burials (Lee-Kwan et al, 2017). The research focussed on seven chiefdoms in Bo District, Sierra Leone over the week of October 20, 2014, and identified cultural barriers to clinically safe burial practices. These insights were then used to guide emergency response teams and shared with aid workers and humanitarian agencies within the month, enabling them to have impactful dialogue with affected communities and implement culturally appropriate changes toward safer burial practices that slowed the spread of the disease. 


Here, we can see that a quantitative approach was crucial in proving the theory that EVD was transmitted within populations, and it provided a strong rationale for a policy-based response by Sierra Leonean authorities. However, it was a qualitative approach that facilitated quick and contextually appropriate change in affected communities and rapidly reduced the spread of EVD. 


Quantitative approaches can provide means to accurately identify populations for research and aid, as in the case of the Nepalese earthquake in 2015. Wilson et al (2016) utilised cell phone tower data in Nepal to rapidly identify displacement as a result of the earthquake. By comparing cell tower connection data from before and after the earthquake, and correlating this with seismic data, researchers located vulnerable communities and identified populations that were displaced. This data was invaluable in directing and triangulating aid as well as further qualitative studies on the impact of the earthquake (Yabe et al, 2020). 


Whilst the use of cell tower data was a rapid way of directing aid to displaced populations, as with all quantitative research, the data may not tell the whole story. Not everyone in Nepal owned a mobile phone: this data was a proxy for the presence of people, and may not reflect the actual density or size of populations. Additionally, akin to The Streetlight Effect (see figure 1) – a drunk man searches for his keys under the streetlight not because that’s where he lost his keys but because that’s where the light is brightest (Fisher, 1942) – it’s critical to distinguish areas where there was no cell tower from areas where there are no people. As a result of these two effects, aid and research may have been directed not to where the need was greatest, but to where mobile phone ownership was highest.

the streetlight effect

Figure 1: The Streetlight Effect: June 1942, a comic strip called Mutt & Jeff, by B Fisher. 

Quantitative research typically demonstrates generalisability across different contexts to the original study. Both previously mentioned studies demonstrate generalisability, however, as shown, quantitative research often lacks depth and context. In the field of global health, the results of quantitative research may tell us that a decision or action needs to be taken, but not how to do it. In this respect, qualitative approaches can provide rapid and actionable insights. Qualitative research methods have an unrivalled capacity to constitute compelling arguments about how things work in particular contexts (Mason, 2002). They allow us to “explore the perspectives, experiences, relationships and decision-making processes of human actors within health systems, and in so doing, help uncover and explain the impact of vital but difficult-to-measure issues such as power, culture and norms” (Topp et al, 2018, p. 2). 


One of the strengths of quantitative research is it is considered to be objective, and thus, more reliable as a foundation for decision making. Numerical data is perceived as the “exclusion of judgement, the struggle against subjectivity” (Porter, 1997, p.ix), and numbers are regarded as hard – thus a safer bet for those making business or policy decisions (Muller, 2018). Qualitative data, partly as a result of its low generalisability but also in its perception as being soft in comparison to quantitative data, is sometimes considered less robust as a rationale for important decisions. However, research has shown that quantitative data is actually less effective than qualitative data in persistently changing attitudes (Kazoleas, 1993). Whilst quantitative data may lend itself to justifying policy and business decisions with confidence, it is qualitative data that is necessary to win over hearts and minds in the long term.


Quantitative data can also lend itself well to visualisation. Data visualisation represents any representation of information designed to enable communication, analysis, discovery, or exploration (Cairo, 2016). However, “…it is important for creators and readers of these depictions to remember that they are not ‘data’ but readings, interpretations of data meditated by programmed algorithms and hermeneutic desires.” (Wulfman, 2014, p94). That is to say, data visualisations can be as misleading as they can be enlightening. As Rosemary Hill points out, “…data is often reified as objective, by showing how the rhetoric of objectivity within data visualisation conventions is harnessed to do work in the world that is potentially very damaging to women’s rights” (Hill, 2017, p.83). Hill showed that anti-abortion groups were more likely to utilise data visualisation than pro-choice groups, which worked to ostensibly simplify the issue and “mobilise the rhetoric of neutrality” (Ibid, p.83). Data visualisations can frame issues in persuasive ways, potentially leading to overconfident impressions of causality and prioritising values such as scientific objectivity over others. These aspects can influence audiences’ thoughts and actions on important issues, and may contribute to misinformation or misrepresentation (Nash, Trott and Allen, 2022), leading to adverse impacts on public health such as restriction of access to abortion services as highlighted by Hill (2017).


One of quantitative data’s most powerful strengths is the ability to identify trends and patterns over time. An impactful application of this is Statistical Process Control (SPC), a practice spawned at Bell Laboratories in 1920 by Walter Shewhart, which is only now beginning to shape global health research. Shewhart brought together the disciplines of statistics, engineering, and economics and became known as the father of modern quality control (ASQ, 2000). Shewhart measured the quality output of manufacturing processes and mapped this data onto an SPC chart over time, an example of which is shown below. Through this, trends and anomalies can be identified (Seland, 2021). SPC detects changes in a process over time: a trend up or down indicates a cause “common” to the process, whilst an anomaly indicates a “special” cause – one that is not common to the process, such as a power outage or equipment failure. In most applications of SPC, anything within three sigmas (one sigma is one standard deviation) of the mean is considered to be a common cause, whilst anything outside that range is considered a special cause. 

SPC test chart

Figure 2: An example SPC Chart showing a trend upwards (common cause) and a spike above three sigmas (special cause). Source: WEAHN.


SPC charts revolutionised quality in industry, and have become increasingly utilised in health contexts (Carey and Lloyd, 1995). Researchers Mduma et al (2018) recently used SPC charts in labour wards and operating theatres at Haydom Lutheran Hospital in north-central Tanzania, to trace changes in perinatal mortality after a training program. The study, one of the first of its kind in a rural Sub-Saharan hospital, revealed a steady improvement in survival over time along with variations that could be linked to different interventions and events. As this example shows, SPC is a powerful tool to continuously monitor and describe changes in patient outcomes. SPC is “one of very few statistical methods that complete the hypothesis generation–hypothesis testing cycle of the scientific method, which is one reason for its popularity with practitioners. Practitioners have found that they learn new information from the charts, rather than just making a ‘yes/no’ decision.” (Mohammed, 2004, p. 244). It’s believed that the use of SPC would have identified the crimes of Harold Shipman as early as 1984, saving the lives of around 175 people (Spiegelhalter, 2019). SPC is not only able to provide methods for early detection of illegal behaviour, but also help to inform the quality of clinical care and maintain public trust (Aylin et al, 2003). 


However, the human experience of health, illness and wellbeing is much more than biomedical findings and statistics, and cannot adequately be described by numbers or trends – “not everything that counts can be counted” (Cameron, 1963, p. 13). Indeed, the act of measurement itself can affect behaviour, particularly if people subject to the measurement are aware of it. The classic example of this is the “Cobra Effect”. Anecdotally, The British Colonial Government in Delhi, India implemented a bounty program to decrease the population of cobras. Initially successful in reducing the number of observed cobras, the scheme demonstrated a paradoxical effect over time: an increase in the presentation of dead cobras for the bounty was observed, not due to increased captures but due to entrepreneurial individuals breeding cobras to exploit the financial incentives. Recognizing this unintended consequence, government officials terminated the program, leading breeders to release the cobras into the wild, ultimately resulting in a higher cobra population in Delhi compared to pre-program levels (Hollins, 2017). In a similar vein there is Goodhart’s Law: When a measure becomes a target, it ceases to be a good measure, and the evolved version, Campbell’s Law: The more important a metric is in social decision making, the more likely it is to be manipulated. In health, these effects have been observed to have damaging implications in health research (Chong, 2021), healthcare service delivery (Poku, 2016) and waiting times for patients (Crawford, 2017). If we are to measure important things, we must be careful not to create perverse incentives by doing so. 


Another aspect of quantitative data that must be treated with care is the difference between precision and accuracy; confusing the two can result in misinterpretation of the data. It is easy to believe that a very precise measure is more accurate than a less precise measure. In fact, there is no such correlation. Indeed, many articles refer to the reliability of a scale as its ‘‘precision,’’ and validity as its ‘‘accuracy’’, which is unhelpful and misleading (Streiner & Norman, 2006). Reliability is the ability of a measurement to obtain the same measure each time, whilst validity is the degree to which the method measures what you really want to measure (Spiegelhalter, 2019). An example of where validity may be misinterpreted is the Ecological Fallacy, or an “omitted-variables bias” (Kögel, 2017), where population level data is used to make erroneous assumptions about individual effects, a variety of which is Simpson’s Paradox. Simpson’s paradox is when a trend appears in several different groups of data but disappears or even reverses when groups are combined. Without careful statistical analysis and contextual understanding, quantitative data can disguise the truth, leading to disastrous consequences for public health. For example, during the early Covid-19 pandemic, early epidemiologic data showed that the case fatality rate was higher in Italy than in China. However, in analysing the data segmented by age, case fatality rates in China were higher compared to Italy across all age groups, because the distribution of cases across age groups differed significantly between the two countries (von Kügelgen et al, 2021).


Given the weaknesses demonstrated by quantitative methodologies, combining qualitative and quantitative methods can be powerful, and is the basis behind the mixed methods approach. The rationale of which is that through combining qualitative and quantitative methods in a multidisciplinary approach, one can utilise their respective strengths and escape their respective weaknesses (Tashakkori & Teddlie, 1998, from Lund, 2012). 

quantitative and qualitative research methods

Figure 3: How Qualitative and Quantitative approaches can complement each other in a mixed methods approach. 


As illustrated in my diagram above, the integration of quantitative and qualitative research methods enables a complementary approach to hypothesis generation, validation, and refutation. Qualitative methods allow researchers to observe and identify patterns that suggest a potential causal relationship between variables. Conversely, quantitative methods allow for the testing and verification or rejection of these causal links. Additionally, quantitative analyses may reveal correlations between variables, which can be further explored using qualitative methods to provide insights into the underlying mechanisms and reasons behind the observed associations (Schoonenboom & Johnson 2017). Combining both approaches allows the researcher to lend greater context to data: “Mixed-methods evaluations which integrate statistical methods with qualitative research methods are powerful… they allow [us] to peek into the black box.” (Alba et al, 2020, no page). A mixed methods approach is now recommended across the global health research field, from community health research (Andrew & Halcomb, 2007) to epidemiological studies (​​Lane-Fall, 2023). As demonstrated by the cellphone data in Wilson et al (2016) data in isolation only tells one story – that of population displacement – but when combined with qualitative approaches, the combined triangulated information is Aristotelian: the whole is greater than the sum of its parts. 


The future holds opportunities to improve quantitative and qualitative approaches to research into global health. Increasing computing power and the ability to store massive datasets hold huge potential for quantitative research (Franklinos et al, 2022). Large Language Models (LLMs) already show promise in deductive coding (Xiao et al, 2023), analysis of massive datasets, the ability to rapidly generate statistical code and conduct rapid literature reviews (Sallam, 2023). LLM tools such as Remesh allow researchers to collect and process qualitative data faster and more easily. However, with that efficiency comes the danger of blind faith in the technology: trusting the outputs without question, potentially leading to worse research outcomes (Ellis & Reich, 2023) and poor public health decision making.


Emergent (at least in global health) practices such as SPC are likely to change the way global health research is conducted in the future. The trend towards mixed methods will continue, enhanced by the capabilities of LLMs, big data and cloud computing. It is clear that quantitative methods have their strengths, but also weaknesses. Applying both quantitative and qualitative approaches in mixed methods studies has the potential to allow researchers to utilise both their strengths and mitigate their respective weaknesses. 


Word count: 2994.




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Disaster Cycle for an Earthquake in Nepal:

Having spent some time in Nepal a year or so after the earthquake, and knowing people there who were, and still are, impacted by the disaster, I’ve created a disaster management cycle for an earthquake in Nepal.

As Kahn (2008) states, “disaster occurs only when hazards and vulnerability meet“, and that vulnerability can be broken down into physical and socioeconomic vulnerabilty. Nepal, prior to the earthquake and still to this day, was both physically vulnerable due to poor infrastructure and low building standards, and also socio-economically vulnerable, comparable in economic status to Haiti and Cambodia (see chart below). Hopes that the country would be able to “build back better” as per the Sendai framework have been hindered significantly by the Covid-19 pandemic (Adhikari, 2020), and progress is slow.


Disaster Cycle for an Earthquake in Nepal:


Mitigation and prevention:

Improving building codes along with proper enforcement, plus modernising of vulnerable buildings.

Running earthquake drills and training for emergency services and communities

Land-use planning to minimize exposure to hazards: e.g. by ensuring power stations are not in vulnerable locations.



Developing early warning and public communication systems

Ensuring adequate supplies of emergency equipment and medical supplies

Creating evacuation plans for vulnerable communities


Response and relief:

Activating search and rescue teams

Providing medical aid, clean water and food

Establishing temporary accomodation for displaced people



Reconstructing damaged buildings

Providing essential services such as water and electricity

Providing psychological support and therapy for affected people, especially hidden or marginalised communities



Rebuilding permanent housing and community structures to higher standards, and to better withstand future earthquakes

Financial support for affected individuals and business grants to revitalise local economies.

Making regular assessments of progress and documenting lessons learned



Adhikari, B. et al. (2020) “Earthquake rebuilding and response to COVID-19 in Nepal, a country nestled in multiple crises”, Journal of Global Health, 10(2). doi: 10.7189/jogh.10.020367.

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Reflections on the Antecedents to the Fukushima Disaster in 2011

The Fukoshima “triple disaster” in 2011 was caused by an earthquake that triggered a tsunami, which then hit nuclear power stations situated on the East Coast of Japan. This resulted in large quantities of radioactivity released into the natural environment, and more than 300,000 residents evacuated as well as a cleanup operation that may take decades and cost hundreds of billions of dollars. (Reconstruction Unit Secretariat, 2012). Prior to the disaster, post-WW2, Japan had pushed to become energy independent, rather than historically reliant on energy imports, particularly from the Middle East (World Nuclear Association, 2023). This resulted in great political and commercial pressure to fund and build nuclear plants, further enhanced by the 1992 Kyoto protocol to reduce greenhouse gas emissions.


In 2007, Katsuhiko Ishibashi wrote an article titled “Why Worry? Japan’s Nuclear Plants at Grave Risk From Quake Damage”, highlighting the damage caused to the Kashiwazaki-Kariwa Nuclear Power plant operated by Tokyo Electric Power Co. (TEPCO) as a result of an earthquake. Ishibashi even included a map, showing the two Fukushima plants and highlighting their vulnerability, positioned in areas of seismic instability: “…the guidelines should require that a nuclear power plant, no matter where it is located, should be designed to withstand at least the ground acceleration caused by an earthquake of about a 7.3 magnitude, roughly 1000 gal. In fact, however, the new guidelines require only about 450 gal.” (Ishibashi, 2007) The Chairman of Japan’s nuclear Safety Commission at the time, Haruki Madarame, dismissed Ishibashi’s claims because he was a “nobody”. (Clenfield, 2011)

Map showing location of the Kashiwazaki-Kariwa plant. Ishibashi, 2011.

The Fukushima disaster could have been prevented. Ishibashi was acting as a people-centred Early Warning System, and yet, due to political pressures plus a fracture between academia, and industry and government, (Edmondson, 2018) he was dismissed as a “nobody” and his early warnings of a major disaster were not heeded.


The same dynamics unfold in the case of the flash floods that occurred in Chamoli District in India in February 2021, which Chandi Prasad Bhatt had warned of in essays written over the 1980’s, 1990’s, and 2000s (Guha, 2021). As Guha points out, decisions regarding where, what, and how to build infrastructure and housing should “involve the best scientists in the country” and be decoupled from political pressures and incentives that can hamper decision making.




Clenfield, J. 2011. Nuclear Regulator Dismissed Seismologist on Japan Quake Threat

Available at: https://www.bloomberg.com/news/articles/2011-11-21/nuclear-regulator-dismissed-seismologist-on-japan-quake-threat?leadSource=uverify%20wall#xj4y7vzkg (Accessed: 27 February 2023).


Edmondson, Amy C. The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. Hoboken, NJ: John Wiley & Sons, 2018.


Guha, R. 2021. 6 lessons from a Himalayan tragedy. https://www.ndtv.com/opinion/6-lessons-from-a-himalayan-tragedy-by-ramachandra-guha-2365783 (Accessed: 27 February 2023).


Ishibashi, K., 2007. Why Worry? Japan’s Nuclear Plants at Grave Risk From Quake Damage. International Herald Tribune, 11.


Nuclear Power in Japan | Japanese Nuclear Energy – World Nuclear Association . (2023). Retrieved 27 February 2023, from https://world-nuclear.org/information-library/country-profiles/countries-g-n/japan-nuclear-power.aspx

Reconstruction Unit Secretariat, “Report on the Number of Evacuees Across the Country, Prefectural and Other Refugees,” February 1, 2012, (2023). Retrieved 27 February 2023, from https://www.reconstruction.go.jp/topics/20120201zenkoku-hinansyasu.pdf

Preparedness and Disaster

Drawing on the lecture contents, share one example of a real-world disaster caused by people’s under- or unpreparedness. Consider both behavioural and systemic/structural causes for under or unpreparedness.


On april 26, 1986, the RBMK-1000 reactor at Chernobyl suffered a series of catastrophic failures that resulted in core meltdown. The RBMK type of reactor was inherently unsafe compared to most reactors used in Europe and the USA, partly because of a lack of concrete reactor shielding and partly due to the use of cheaper and more powerful graphite as a moderator instead of water (IAEA, 1992). The Soviet government at the time maintained that nuclear power was perfectly safe, despite multiple previous nuclear power incidents in the country, and that accidents were unlikely to the point of impossible (Plokhy, 2018). This stance led to a lack of safety protocols and a belief that spending extra money to build concrete shielding for the reactor in case of a meltdown was unnecessarily wasteful.


After the explosions at the plant, government officials, afraid of contradicting their superiors in the party, resisted calling for evacuation of local towns for many hours and days. In the nearby town of Kharkov, the International Workers’ Day parade took place on May 1, 1986 despite radiation levels many times the levels required to trigger evacuation (Ervasti, 1986).


Radiation is invisible, and that combined with government assurances that nuclear power was perfectly safe, meant that people in Chernobyl and surrounding towns did not prepare for such an eventuality. When the disaster occurred, some people were even found to be sunbathing, having discovered that they tanned quickly (not realising that the “tan” was in fact deadly radiation burns) (BBC, 2019).


Even emergency responders such as firefighters and the military, did not appreciate the seriousness of heavy doses of radiation, and were seen picking up bits of graphite from the reactor that had been thrown into the air by the explosion (Alexakhin, et al. 2006). For some people involved in the response, their belief that nuclear power was safe was so strong as to dismiss any safety concerns, due to the edicts issued by the Soviet government.


People are less likely to prepare for a disaster in high compliance cultures where authorities dismiss concerns and overstate safety, and radiation, being an invisible threat, compounds the issue.


Share one example of disaster risk reduction alternative that has either improved or has the potential to improve people’s preparedness. Consider both potential and limitations of such alternative.


In Bangladesh, an approach of community-based disaster risk management (CBDRM) has been implemented in response to the threat of floods and cyclones. (ADPC, 2008) This involves local communities in identifying and assessing their own disaster risks, developing preparedness and response plans, and implementing risk reduction measures. The program in Chittagong  involves training community members in disaster risk reduction techniques, including early warning, evacuation, and first aid. Community members are also involved in the planning and implementation of disaster risk reduction measures, such as building raised platforms for homes and livestock, constructing flood shelters, and planting trees to prevent erosion.


Through this program, communities have become more resilient to disasters, and have been able to reduce the impact of floods and cyclones on their homes, crops, and livelihoods. Additionally, the program has empowered local communities to take ownership of their own disaster risk reduction efforts and has improved communication and coordination between community members and local authorities (Shaw, 2006).


CBDRM programs may not always involve all members of the community equally however. Some groups, such as women, children, and people with disabilities, may be excluded from planning and implementation, which can lead to unequal distribution of resources and a lack of representation in decision-making. A lack of technical expertise can also hamper the effectiveness of CBDRM programs and may result in inadequate or ineffective disaster risk reduction measures (Nguyen et al, 2020).


Word count: 615




ADPC, 2008. “Community Empowerment and Disaster Risk Reduction in Chittagong City” Safer Cities 21. Available at: https://www.preventionweb.net/files/globalplatform/entry_bg_paper~SaferCities21.pdf (Accessed: 13 March 2023).


Shaw, R., 2006. Critical issues of community based flood mitigation: examples from Bangladesh and Vietnam. Science and Culture, 72(1/2), p.62.


“INSAG-7: The Chernobyl Accident: Updating of INSAG-1” (PDF). IAEA. 1992. Archived (PDF) from the original on 20 October 2018. Available at: https://www-pub.iaea.org/MTCD/publications/PDF/Pub913e_web.pdf (Accessed: 13 March 2023).


Plokhy, S. (2018). Chernobyl: the history of a nuclear catastrophe. First edition. New York, Basic Books.


Ervasti, R, (1986) Soviets Celebrate May Day, No Mention of Nuclear Accident With AM-May Day. AP News. Available at: https://apnews.com/article/595cefb4d64896ee1a422caa6c1b88d2 (Accessed: 13 March 2023).


The Aftermath of the 1986 Chernobyl Disaster: An Eyewitness Account | BBC HistoryExtra (2019). Available at: https://www.historyextra.com/period/20th-century/chernobyl-disaster-ukraine-when-what-first-responders-diary-eyewitness-nuclear-exclusion-zone/ (Accessed: 13 March 2023).


Alexakhin, R.M. et al. (2006). Environmental consequences of the Chernobyl accident and their remediation: Twenty years of experience. Report of the Chernobyl Forum Expert group “Environment”.


Nguyen, H., Pross, C., Han, J Y-C. (2020). (Ed) Perkins, M. Review of gender-responsiveness and disability-inclusion in disaster risk reduction in Asia and the Pacific. UN Women-Asia and the Pacific. https://asiapacific.unwomen.org/en/digital-library/publications/2020/10/ap-review-of-gender-responsiveness-and-disability-inclusion-in-drr

Resilience and Disaster Response and Recovery

  1. Alexander (2013, p.2714) argues “resilience has a bright future ahead of it as an explanatory concept in various allied fields that deal with environmental extremes. However, its success in this respect will depend on not overworking it or expecting that it can provide more insight and greater modeling capacity than it is capable of furnishing.” In the context of DRR, do you agree with this statement? Provide reasons for why/why not.


Resilience is not a panacea for all the challenges facing DRR. Resilience may be misinterpreted to mean robustness: simply sustaining functions despite challenges, whilst true resilience is “The intrinsic ability of a system to adjust its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions.” (Hollnagel et al, 2006) Rather than treat resilience as a model, we must treat resilience as an activity, and make clear the distinction between active resilience and passive robustness. 


The scholar Nassim Taleb, in his book “Antifragile” notoriously misinterpreted resilience: “Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better.” (Taleb, 2013) It is clearly an easily misinterpreted and misunderstood concept.


Additionally, as Cretney (2014) argues, there is a danger that resilience can become a tool for the perpetuation of inequalities and injustices. We must question for whom resilience is being built and who benefits from it most.


  1. Given the range of vulnerabilities associated with New Orleans and Hurricane Katrina (2005), as outlined by Laska and Morrow (2006), suggest THREE disaster preparedness measures or initiatives that could serve to reduce some of these vulnerabilities.


Applying the concept of multi-layer safety (MLS) by Esteban et al, we can highlight three disaster preparedness elements that may have served to reduce some of the vulnerabilities highlighted by Laska and Morrow (2006) are:


  1. Primary layer: Hurricane Katrina highlighted the importance of adequate infrastructure in reducing both the likelihood and the severity of flooding. This includes improved infrastructure, such as stronger levees and better drainage systems, to reduce the likelihood of future floods.
  2. Secondary layer: During Hurricane Katrina, communication between emergency responders, local officials, and the public was limited, leading to confusion and delays in response. These gaps in emergency response planning demonstrated the need for effective early warning systems. The appropriate measures include developing a comprehensive early warning system and up to date evacuation plans.
  3. The Tertiary layer regards recovery and restoration after a disaster. In the case of Hurricane Katrina, better involvement of the local community in planning for a disaster, and responding to it afterwards would improve the effectiveness of recovery programmes. 


  1. Given the many and varied critiques and limitations of the resilience concept (e.g. as presented in Cretney 2014), why do you think resilience persists as a central element and goal of DRR strategies?


Resilience is about what a system can do — including its capacity: 


  • to anticipate — seeing developing signs of trouble ahead to begin to adapt early and reduce the risk of decompensation 
  • to synchronize —  adjusting how different roles at different levels coordinate their activities to keep pace with tempo of events and reduce the risk of working at cross purposes 
  • to be ready to respond — developing deployable and mobilizable response capabilities in advance of surprises and reduce the risk of brittleness 
  • for proactive learning — learning about brittleness and sources of resilient performance before major collapses or accidents occur by studying how surprises are caught and resolved 


(From Woods, 2018.) As Woods states, resilience is a verb. There are many aspects of resilience, but the key point of adapting and improving is vital. DRR is an active process, it is a verb, just like resilience. DRR exists to serve people, and the adaptable element of resilience is people: it is only people who can adapt, learn, and improve (Geraghty, 2020), which is why resilience persists as a central tenet of DRR.




Cretney, R. 2014. Resilience for whom? Emerging critical geographies of socio-ecological resilience. Geography Compass, 8 (9): 627–40.


Esteban, M. et al (2013). Recent tsunamis events and preparedness: Development of Tsunami Awareness in Indonesia, Chile and Japan, International Journal of Disaster Risk Reduction. Elsevier. Available at: https://www.sciencedirect.com/science/article/pii/S221242091300037X (Accessed: February 16, 2023).


Resilience Engineering and DevOps – A Deeper Dive | Tom Geraghty (2020). Available at: https://tomgeraghty.co.uk/index.php/resilience-engineering-and-devops/ (Accessed: 16 February 2023).


Hollnagel, E., Woods, D. D. & Leveson, N. C. (Eds.) (2006). Resilience engineering: Concepts and precepts. Aldershot, UK: Ashgate.


Laska, S., and Morrow, B. H. (2006). Social vulnerabilities and Hurricane Katrina: an unnatural disaster in New Orleans. Marine technology society journal, 40(4), 16-26.


Taleb, Nassim Nicholas. 2013. Antifragile. Harlow, England: Penguin Books.


Woods, D.D., 2018. Resilience is a verb. Domains of resilience for complex interconnected systems., p.167.